{"id":227133,"date":"2026-07-25T17:16:19","date_gmt":"2026-07-25T17:16:19","guid":{"rendered":"https:\/\/www.9senses.ai\/?page_id=227133"},"modified":"2026-08-02T18:34:01","modified_gmt":"2026-08-02T18:34:01","slug":"what-is-ai","status":"publish","type":"page","link":"https:\/\/www.9senses.ai\/de\/what-is-ai\/","title":{"rendered":"Was KI wirklich ist"},"content":{"rendered":"<div class=\"et_pb_section_0 et_pb_section et_section_regular et_block_section preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_row_0 et_pb_row et_block_row ns-hdr preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_0 et_pb_column et_pb_column_2_3 et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_0 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h4>9senses on artificial intelligence<\/h4>\n<\/div><\/div><div class=\"et_pb_text_1 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h1>What AI really is...<\/h1>\n<\/div><\/div><div class=\"et_pb_text_2 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>When most of us at 9senses began working with what is now labeled <strong>Artificial Intelligence,<\/strong> we didn't use that term. Back then, we were talking about non-linear computing, fuzzy logic, heuristics, machine learning, among others.<\/p>\n<p>Today, many people think that AI makes computers as smart as humans. In reality, computer software is still far away from reaching that level, but today it is able to <a href=\"\/why-9senses\">emulate and even surpass human capabilities in specific fields<\/a>, particularly those that require the processing of large amounts of information or the generation of output from a large data pool. We would like to instill a bit of clarity here, at the cost of taking some of the magic of AI away, as did Joseph Weizenbaum, the legendary creator of <a href=\"#eliza\">Eliza:<\/a><\/p>\n<\/div><\/div><\/div><div class=\"et_pb_column_1 et_pb_column et_pb_column_1_3 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_code_0 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\"><div class=\"ns-vmap-editor-note\" style=\"position:relative;width:100%;overflow:hidden;aspect-ratio:5\/4;max-height:230px;\"><div style=\"position:absolute;inset:0;display:flex;align-items:center;justify-content:center;pointer-events:none\"><div style=\"padding:10px 12px;border:1px dashed rgba(170,180,205,.55);border-radius:6px;color:#8a93a6;font:600 12px\/1.4 sans-serif;background:rgba(8,13,25,.46)\"><div style=\"margin-bottom:6px\">Vector map \u2014 Main<\/div><code style=\"display:inline-block;padding:3px 6px;border-radius:4px;background:rgba(127,140,170,.12);color:inherit\"><div class=\"ns-vmap-editor-note\" style=\"position:relative;width:100%;overflow:hidden;aspect-ratio:5\/4;max-height:230px;\"><div style=\"position:absolute;inset:0;display:flex;align-items:center;justify-content:center;pointer-events:none\"><div style=\"padding:10px 12px;border:1px dashed rgba(170,180,205,.55);border-radius:6px;color:#8a93a6;font:600 12px\/1.4 sans-serif;background:rgba(8,13,25,.46)\"><div style=\"margin-bottom:6px\">Vector map \u2014 Main<\/div><code style=\"display:inline-block;padding:3px 6px;border-radius:4px;background:rgba(127,140,170,.12);color:inherit\">[ninesenses_vectormap #1]<\/code><div style=\"margin-top:6px;font-weight:400\">Max height: 230px. Preview on the live page.<\/div><\/div><\/div><\/div><\/code><div style=\"margin-top:6px;font-weight:400\">Max height: 230px. Preview on the live page.<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"et_pb_section_1 et_pb_section et_section_regular et_block_section preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_row_1 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_2 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_3 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><blockquote style=\"border-left:0;padding-left:0;margin:30px 0 8px 0;text-align:right\"><p>\u201cWhat I had not realized is that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people.\u201d<\/p><\/blockquote>\n<p style=\"text-align:right\"><span style=\"font-family:'Open Sans';font-weight:normal\">Joseph Weizenbaum (1923-2008), Inventor of Eliza<\/span><\/p>\n<\/div><\/div><\/div><\/div><\/div><div class=\"et_pb_section_2 et_pb_section et_section_regular et_block_section ns-block preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\" lang=\"en\" style=\"max-width:1080px;margin-left:auto;margin-right:auto;border-radius:0;hyphens:auto;-webkit-hyphens:auto;-ms-hyphens:auto\" id=\"History_of_AI\"><div class=\"et_pb_row_2 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_3 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_4 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2 style=\"display: inline; margin: 0 0.6em 0 0; padding: 0;\">History of AI<\/h2>\n<p><span>The idea of a machine-driven intelligence is not new. Literature has come up with speaking automatons way before the steam engine was invented, and since the arrival of computers, we have hoped for and <a href=\"\/can-ai-end-humanity\">feared AI smarter than humans<\/a>.<\/span><\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_3 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_4 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_code_1 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\">\t<div id=\"ns-aih-LjOtpWw\" class=\"ns-aih ns-aih-theme-dark\"\n\t\tdata-autoplay=\"yes\"\n\t\tdata-interval=\"8000\"\n\t\tdata-start=\"0\"\n\t\tstyle=\"--ns-aih-accent:#58a7f9;--ns-aih-secondary:#0c71c3;--ns-aih-pane-min:240px;\">\n\n\t\t<div class=\"ns-aih-strip-wrap\">\n\t\t\t<div class=\"ns-aih-rail\" role=\"tablist\" aria-label=\"AI history milestones\">\n\t\t\t\t<div class=\"ns-aih-rail-track\">\n\t\t\t\t\t<div class=\"ns-aih-rail-line\" aria-hidden=\"true\"><\/div>\n\t\t\t\t\t<canvas class=\"ns-aih-rail-canvas\" aria-hidden=\"true\"><\/canvas>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\"\n\t\t\t\t\t\t\tclass=\"ns-aih-dot is-active\"\n\t\t\t\t\t\t\tdata-index=\"0\"\n\t\t\t\t\t\t\tstyle=\"left:0%\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"true\"\n\t\t\t\t\t\t\taria-label=\"1816 - Fiction\">\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-core\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-year\">1816<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\"\n\t\t\t\t\t\t\tclass=\"ns-aih-dot\"\n\t\t\t\t\t\t\tdata-index=\"1\"\n\t\t\t\t\t\t\tstyle=\"left:49.556%\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"1950 - The Turing Test\">\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-core\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-year\">1950<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\"\n\t\t\t\t\t\t\tclass=\"ns-aih-dot\"\n\t\t\t\t\t\t\tdata-index=\"2\"\n\t\t\t\t\t\t\tstyle=\"left:60.317%\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"1966 - Eliza\">\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-core\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-year\">1966<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\"\n\t\t\t\t\t\t\tclass=\"ns-aih-dot\"\n\t\t\t\t\t\t\tdata-index=\"3\"\n\t\t\t\t\t\t\tstyle=\"left:73.097%\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"1980s - Machine Learning\">\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-core\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-year\">1980s<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\"\n\t\t\t\t\t\t\tclass=\"ns-aih-dot\"\n\t\t\t\t\t\t\tdata-index=\"4\"\n\t\t\t\t\t\t\tstyle=\"left:79.822%\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"1990s - Playing Chess (and Winning)\">\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-core\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-year\">1990s<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\"\n\t\t\t\t\t\t\tclass=\"ns-aih-dot\"\n\t\t\t\t\t\t\tdata-index=\"5\"\n\t\t\t\t\t\t\tstyle=\"left:86.548%\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"2000s - Seeing and Knowing\">\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-core\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-year\">2000s<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\"\n\t\t\t\t\t\t\tclass=\"ns-aih-dot\"\n\t\t\t\t\t\t\tdata-index=\"6\"\n\t\t\t\t\t\t\tstyle=\"left:93.274%\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"2010s - Solving Complex Problems\">\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-core\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-year\">2010s<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\"\n\t\t\t\t\t\t\tclass=\"ns-aih-dot\"\n\t\t\t\t\t\t\tdata-index=\"7\"\n\t\t\t\t\t\t\tstyle=\"left:100%\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"2020s - Listening and Speaking\">\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-core\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t\t\t<span class=\"ns-aih-dot-year\">2020s<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<button type=\"button\" class=\"ns-aih-playtoken\" aria-pressed=\"true\">\n\t\t\t\t\t<span class=\"ns-aih-icon-play\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t<span class=\"ns-aih-icon-pause\" aria-hidden=\"true\"><\/span>\n\t\t\t\t\t<span class=\"ns-aih-visually-hidden ns-aih-label-play\">Play<\/span>\n\t\t\t\t\t<span class=\"ns-aih-visually-hidden ns-aih-label-pause\">Pause<\/span>\n\t\t\t\t<\/button>\n\t\t\t<\/div>\n\t\t<\/div>\n\n\t\t<div class=\"ns-aih-pane\">\n\t\t\t<div class=\"ns-aih-slides\">\n\t\t\t\t\t\t\t\t\t<section class=\"ns-aih-slide is-active\" data-index=\"0\" aria-hidden=\"false\">\n\t\t\t\t\t\t<h3 class=\"ns-aih-slide-title\">Fiction<\/h3>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-scene\">\n\t\t\t\t\t\t\t    <section\n        class=\"aihm-scene aihm-scene--automaton\"\n        id=\"ns-aih-LjOtpWw-0-automaton\"\n        data-aihm-scene=\"automaton\"\n        data-autoplay=\"1\"\n        style=\"--aihm-height:260px\"\n    >\n        <div class=\"aihm-frame\">\n            \t<figure class=\"aihm-visual aihm-automaton\" aria-label=\"Close view of the Jaquet-Droz Writer automaton moving its writing hand, quill, and head above the writing surface\">\n\t\t<img\n\t\t\tclass=\"aihm-automaton-image\"\n\t\t\tsrc=\"https:\/\/www.9senses.ai\/wp-content\/plugins\/ninesenses-ai-history\/assets\/images\/automaton-writer-poster-v032.webp\"\n\t\t\tdata-aihm-poster-src=\"https:\/\/www.9senses.ai\/wp-content\/plugins\/ninesenses-ai-history\/assets\/images\/automaton-writer-poster-v032.webp\"\n\t\t\tdata-aihm-motion-src=\"https:\/\/www.9senses.ai\/wp-content\/plugins\/ninesenses-ai-history\/assets\/images\/automaton-writer-motion-v032.webp\"\t\t\talt=\"The Jaquet-Droz Writer automaton with its writing surface, quill, and hands in view\"\n\t\t\tloading=\"lazy\"\n\t\t\tdecoding=\"async\"\n\t\t>\n\t\t<figcaption class=\"aihm-credit\">\n\t\t\t<a class=\"aihm-credit-link\" href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Jaquet_Droz_automata_-_Writer.jpg\" target=\"_blank\" rel=\"noopener noreferrer nofollow\" aria-label=\"View image credit and license details on Wikimedia Commons\">Image credit &amp; license<\/a>\n\t\t<\/figcaption>\n\t<\/figure>\n\t        <\/div>\n    <\/section>\n    \t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-text\">\n\t\t\t\t\t\t\t<p class=\"ns-aih-slide-body\">The idea of &quot;automatons&quot; acting &quot;intelligent&quot; is much older than computers themselves. For example, in E.T.A. Hoffmann&#039;s &quot;The Sandman&quot;, published in 1816, a beautiful girl named Olimpia is introduced. She dances and sings beautifully, but only speaks a few words. In fact, she is an automaton, created by physics professor Spalanzani.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<section class=\"ns-aih-slide\" data-index=\"1\" aria-hidden=\"true\">\n\t\t\t\t\t\t<h3 class=\"ns-aih-slide-title\">The Turing Test<\/h3>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-scene\">\n\t\t\t\t\t\t\t    <section\n        class=\"aihm-scene aihm-scene--turing\"\n        id=\"ns-aih-LjOtpWw-1-turing\"\n        data-aihm-scene=\"turing\"\n        data-autoplay=\"1\"\n        style=\"--aihm-height:260px\"\n    >\n        <div class=\"aihm-frame\">\n            \t<div class=\"aihm-visual aihm-vector-scene aihm-turing-preview\" role=\"img\" aria-label=\"Animated diagram of an interrogator exchanging text messages with two concealed respondents\">\n\t\t<svg class=\"aihm-vector-svg\" viewBox=\"0 0 640 360\" preserveAspectRatio=\"xMidYMid meet\" aria-hidden=\"true\" focusable=\"false\">\n\t\t\t<defs>\n\t\t\t\t<radialGradient 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fill=\"url(#ns-aih-LjOtpWw-1-turing-t-screen)\"\/>\n\t\t\t\t<rect x=\"226\" y=\"260\" width=\"188\" height=\"70\" class=\"aihm-terminal-outline\"\/>\n\t\t\t\t<text class=\"aihm-terminal-title aihm-terminal-title--judge\" x=\"320\" y=\"283\" text-anchor=\"middle\">interrogator<\/text>\n\t\t\t\t<text class=\"aihm-turing-choice\" x=\"320\" y=\"313\" text-anchor=\"middle\">A&nbsp;&nbsp;&nbsp;?&nbsp;&nbsp;&nbsp;B<\/text>\n\t\t\t<\/g>\n\n\t\t\t<circle class=\"aihm-vector-token aihm-vector-token--query\" r=\"5\" fill=\"url(#ns-aih-LjOtpWw-1-turing-t-node)\" filter=\"url(#ns-aih-LjOtpWw-1-turing-t-glow)\" data-route=\"#ns-aih-LjOtpWw-1-turing-t-qa\" data-cycle=\"9000\" data-start=\"450\" data-end=\"2200\" data-radius=\"5\" data-static=\".48\"\/>\n\t\t\t<circle class=\"aihm-vector-token aihm-vector-token--query\" r=\"5\" fill=\"url(#ns-aih-LjOtpWw-1-turing-t-node)\" filter=\"url(#ns-aih-LjOtpWw-1-turing-t-glow)\" data-route=\"#ns-aih-LjOtpWw-1-turing-t-qb\" data-cycle=\"9000\" 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fascination with the technology quickly led to the idea that they could soon perform as intelligently as humans. In 1950, British mathematician and computer scientist Alan Turing devised a test to evaluate when a computer would be able to emulate a human conversation convincingly. It took almost 65 years for the first simulation to narrowly pass.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<section class=\"ns-aih-slide\" data-index=\"2\" aria-hidden=\"true\">\n\t\t\t\t\t\t<h3 class=\"ns-aih-slide-title\">Eliza<\/h3>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-scene\">\n\t\t\t\t\t\t\t    <section\n        class=\"aihm-scene aihm-scene--eliza\"\n        id=\"ns-aih-LjOtpWw-2-eliza\"\n        data-aihm-scene=\"eliza\"\n        data-autoplay=\"1\"\n        style=\"--aihm-height:260px\"\n    >\n        <div class=\"aihm-frame\">\n            \t<a class=\"aihm-visual aihm-action-surface aihm-eliza-preview\" href=\"#eliza\" aria-label=\"Open the 1966 Eliza conversation\">\n\t\t<div class=\"aihm-eliza-paper\">\n\t\t\t<div class=\"aihm-eliza-log\" aria-hidden=\"true\"><\/div>\n\t\t\t<span class=\"aihm-eliza-cursor\" aria-hidden=\"true\">\u258c<\/span>\n\t\t<\/div>\n\t\t<span class=\"aihm-hitarea\" aria-hidden=\"true\">\n\t\t\t<span class=\"aihm-button\">Talk to Eliza<\/span>\n\t\t<\/span>\n\t<\/a>\n\t        <\/div>\n    <\/section>\n    \t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-text\">\n\t\t\t\t\t\t\t<p class=\"ns-aih-slide-body\">When German-American computer scientist Joseph Weizenbaum created chat program &quot;Eliza&quot; in 1966, simulating the dialogue with a psychologist, it was meant like a playful first attempt at processing natural speech. Even though the logic behind it was very simple, many people considered it intelligent and expected computers to be able to speak like humans soon.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aih-slide-link-row\"><a class=\"ns-aih-slide-link\" href=\"#eliza\">Talk to Eliza<span class=\"ns-aih-slide-link-arrow\" aria-hidden=\"true\">&rarr;<\/span><\/a><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<section class=\"ns-aih-slide\" data-index=\"3\" aria-hidden=\"true\">\n\t\t\t\t\t\t<h3 class=\"ns-aih-slide-title\">Machine Learning<\/h3>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-scene\">\n\t\t\t\t\t\t\t    <section\n        class=\"aihm-scene aihm-scene--machinelearning\"\n        id=\"ns-aih-LjOtpWw-3-machinelearning\"\n        data-aihm-scene=\"machinelearning\"\n        data-autoplay=\"1\"\n        style=\"--aihm-height:260px\"\n    >\n        <div class=\"aihm-frame\">\n            \t<div class=\"aihm-visual aihm-vector-scene aihm-ml-preview\" 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class=\"aihm-ml-panel-bg\"\/>\n\t\t\t\t<text class=\"aihm-vector-caption aihm-ml-metric-label\" x=\"500\" y=\"299\">error<\/text>\n\t\t\t\t<text class=\"aihm-ml-error-value\" x=\"582\" y=\"311\" text-anchor=\"end\">0.64<\/text>\n\t\t\t<\/g>\n\t\t\t<text class=\"aihm-ml-backprop-label\" x=\"386\" y=\"312\" text-anchor=\"middle\">weight adjustment \u2190 error<\/text>\n\n\t\t\t<circle class=\"aihm-vector-token aihm-vector-token--forward\" r=\"5\" fill=\"url(#ns-aih-LjOtpWw-3-machinelearning-ml-green)\" filter=\"url(#ns-aih-LjOtpWw-3-machinelearning-ml-glow)\" data-route=\"#ns-aih-LjOtpWw-3-machinelearning-ml-i1h1\" data-cycle=\"8200\" data-start=\"400\" data-end=\"2100\" data-radius=\"5\" data-static=\".38\"\/>\n\t\t\t<circle class=\"aihm-vector-token aihm-vector-token--forward\" r=\"4.5\" fill=\"url(#ns-aih-LjOtpWw-3-machinelearning-ml-green)\" filter=\"url(#ns-aih-LjOtpWw-3-machinelearning-ml-glow)\" data-route=\"#ns-aih-LjOtpWw-3-machinelearning-ml-i2h2\" data-cycle=\"8200\" 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data-route=\"#ns-aih-LjOtpWw-3-machinelearning-ml-h2o1\" data-cycle=\"8200\" data-start=\"2250\" data-end=\"3650\" data-radius=\"4.5\" data-static=\".58\"\/>\n\t\t\t<circle class=\"aihm-vector-token aihm-vector-token--error\" r=\"5\" fill=\"url(#ns-aih-LjOtpWw-3-machinelearning-ml-error)\" filter=\"url(#ns-aih-LjOtpWw-3-machinelearning-ml-glow)\" data-route=\"#ns-aih-LjOtpWw-3-machinelearning-ml-h2o1\" data-cycle=\"8200\" data-start=\"4100\" data-end=\"5500\" data-reverse=\"1\" data-radius=\"5\" data-static=\".72\"\/>\n\t\t\t<circle class=\"aihm-vector-token aihm-vector-token--error\" r=\"4.5\" fill=\"url(#ns-aih-LjOtpWw-3-machinelearning-ml-error)\" filter=\"url(#ns-aih-LjOtpWw-3-machinelearning-ml-glow)\" data-route=\"#ns-aih-LjOtpWw-3-machinelearning-ml-i2h2\" data-cycle=\"8200\" data-start=\"5300\" data-end=\"6800\" data-reverse=\"1\" data-radius=\"4.5\" data-static=\".63\"\/>\n\t\t<\/svg>\n\t<\/div>\n\t        <\/div>\n    <\/section>\n    \t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-text\">\n\t\t\t\t\t\t\t<p class=\"ns-aih-slide-body\">With increasingly powerful computers and larger storage capabilities that were able to handle large datasets, the first successful machine learning approaches were introduced. They were based on the ability to autonomously find patterns in data, relate them back to certain events and conditions and suggest or take action.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<section class=\"ns-aih-slide\" data-index=\"4\" aria-hidden=\"true\">\n\t\t\t\t\t\t<h3 class=\"ns-aih-slide-title\">Playing Chess (and Winning)<\/h3>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-scene\">\n\t\t\t\t\t\t\t    <section\n        class=\"aihm-scene aihm-scene--deepblue\"\n        id=\"ns-aih-LjOtpWw-4-deepblue\"\n        data-aihm-scene=\"deepblue\"\n        data-autoplay=\"1\"\n        style=\"--aihm-height:260px\"\n    >\n        <div class=\"aihm-frame\">\n            \t<div class=\"aihm-visual aihm-deepblue-preview\" role=\"group\" aria-label=\"Deep Blue chess preview\">\n\t\t<div class=\"aihm-db-console\">\n\t\t\t<header class=\"aihm-db-head dbct-header\">\n\t\t\t\t<div class=\"dbct-bars\" aria-hidden=\"true\"><i><\/i><i><\/i><i><\/i><i><\/i><i><\/i><i><\/i><i><\/i><i><\/i><\/div>\n\t\t\t\t<div class=\"dbct-title\" role=\"heading\" aria-level=\"3\">DEEP BLUE<small>RS\/6000 SP &nbsp;\u00b7&nbsp; OPERATOR CONSOLE<\/small><\/div>\n\t\t\t\t<div class=\"dbct-hright\">GAME 6 &nbsp;\u00b7&nbsp; NEW YORK 1997<br>ENGINE: <b>DEEP BLUE<\/b><\/div>\n\t\t\t<\/header>\n\t\t\t<div class=\"aihm-db-layout\">\n\t\t\t\t<div class=\"aihm-db-board\" role=\"img\" aria-label=\"Chess board replaying the opening of game six\"><\/div>\n\t\t\t\t<div class=\"aihm-db-telemetry\">\n\t\t\t\t\t<div>MOVE <b class=\"aihm-db-move\">1. e4<\/b><\/div>\n\t\t\t\t\t<div>PLY <b class=\"aihm-db-search\">1 \/ 10<\/b><\/div>\n\t\t\t\t\t<div>RESULT <b>DEEP BLUE 1\u20130<\/b><\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t<div class=\"aihm-db-controls dbct-controls\">\n\t\t\t\t<button type=\"button\" data-dbct-popup-trigger=\"deepblue\" aria-label=\"Open the Deep Blue chess experience\">Play Chess<\/button>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\t        <\/div>\n    <\/section>\n    \t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-text\">\n\t\t\t\t\t\t\t<p class=\"ns-aih-slide-body\">In 1997, IBM&#039;s Deep Blue supercomputer won its first match against acting chess champion Garry Kasparov. While mostly driven by sheer power which helped build its game on computing more than 200 million positions a second, it was using machine learning elements (heuristics and minimax optimization techniques) mid-game, which can be considered AI.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aih-slide-link-row\"><a class=\"ns-aih-slide-link\" href=\"#deepblue\">Play Chess<span class=\"ns-aih-slide-link-arrow\" aria-hidden=\"true\">&rarr;<\/span><\/a><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<section class=\"ns-aih-slide\" data-index=\"5\" aria-hidden=\"true\">\n\t\t\t\t\t\t<h3 class=\"ns-aih-slide-title\">Seeing and Knowing<\/h3>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-scene\">\n\t\t\t\t\t\t\t    <section\n        class=\"aihm-scene aihm-scene--vision\"\n        id=\"ns-aih-LjOtpWw-5-vision\"\n        data-aihm-scene=\"vision\"\n        data-autoplay=\"1\"\n        style=\"--aihm-height:260px\"\n    >\n        <div class=\"aihm-frame\">\n            \t<div class=\"aihm-visual aihm-vision-real\" role=\"img\" aria-label=\"Animated computer-vision analysis of a cherry image: real image, inverted scan, pixelated scan, then the label cherry with contour overlays\">\n\t\t<div class=\"aihm-vision-photo aihm-vision-photo--clear\" style=\"background-image:url(https:\/\/www.9senses.ai\/wp-content\/plugins\/ninesenses-ai-history\/assets\/images\/vision-cherry-clear-v046.webp)\"><\/div>\n\t\t<div class=\"aihm-vision-layer aihm-vision-layer--invert\">\n\t\t\t<div class=\"aihm-vision-photo aihm-vision-photo--invert\" style=\"background-image:url(https:\/\/www.9senses.ai\/wp-content\/plugins\/ninesenses-ai-history\/assets\/images\/vision-cherry-invert-v046.webp)\"><\/div>\n\t\t<\/div>\n\t\t<div class=\"aihm-vision-layer aihm-vision-layer--pixel\">\n\t\t\t<div class=\"aihm-vision-photo aihm-vision-photo--pixel\" style=\"background-image:url(https:\/\/www.9senses.ai\/wp-content\/plugins\/ninesenses-ai-history\/assets\/images\/vision-cherry-pixel-v046.webp)\"><\/div>\n\t\t<\/div>\n\t\t<div class=\"aihm-vision-layer aihm-vision-layer--final\">\n\t\t\t<div class=\"aihm-vision-photo aihm-vision-photo--final\" style=\"background-image:url(https:\/\/www.9senses.ai\/wp-content\/plugins\/ninesenses-ai-history\/assets\/images\/vision-cherry-final-v072.webp)\"><\/div>\n\t\t\t<div class=\"aihm-vision-tag-wrap\" aria-hidden=\"true\">\n\t\t\t\t<div class=\"aihm-vision-tag-box\">cherry<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t<div class=\"aihm-vision-grid\" aria-hidden=\"true\"><\/div>\n\t\t<div class=\"aihm-vision-scanline aihm-vision-scanline--invert\" aria-hidden=\"true\"><\/div>\n\t\t<div class=\"aihm-vision-scanline aihm-vision-scanline--pixel\" aria-hidden=\"true\"><\/div>\n\t\t<div class=\"aihm-vision-scanline aihm-vision-scanline--final\" aria-hidden=\"true\"><\/div>\n\t<\/div>\n\t        <\/div>\n    <\/section>\n    \t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-text\">\n\t\t\t\t\t\t\t<p class=\"ns-aih-slide-body\">While optical character recognition (OCR) had been developed long ago, computers became capable of &quot;seeing&quot; in the late 20th and the early 21st century. This is when the first face and object recognition systems were developed. By now, AI is able to routinely identify people and objects and also understand what they are doing.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<section class=\"ns-aih-slide\" data-index=\"6\" aria-hidden=\"true\">\n\t\t\t\t\t\t<h3 class=\"ns-aih-slide-title\">Solving Complex Problems<\/h3>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-scene\">\n\t\t\t\t\t\t\t    <section\n        class=\"aihm-scene aihm-scene--deeplearning\"\n        id=\"ns-aih-LjOtpWw-6-deeplearning\"\n        data-aihm-scene=\"deeplearning\"\n        data-autoplay=\"1\"\n        style=\"--aihm-height:260px\"\n    >\n        <div class=\"aihm-frame\">\n            \t<div class=\"aihm-visual aihm-dl-preview\" role=\"img\" aria-label=\"Animated deep-learning process adapted from the 9Senses machine-learning explainer: data becomes vectors, trains a model, produces output, is checked and feeds back\">\n\t\t<canvas class=\"aihm-dl-canvas\" aria-hidden=\"true\"><\/canvas>\n\t\t<div class=\"aihm-dl-node aihm-dl-node--data\" data-dl-step=\"0\">data<\/div>\n\t\t<div class=\"aihm-dl-node aihm-dl-node--vectors\" data-dl-step=\"1\">vectors<\/div>\n\t\t<div class=\"aihm-dl-core\" data-dl-step=\"2\">model<\/div>\n\t\t<div class=\"aihm-dl-node aihm-dl-node--output\" data-dl-step=\"3\">output<\/div>\n\t\t<div class=\"aihm-dl-node aihm-dl-node--check\" data-dl-step=\"4\">check<\/div>\n\t\t<div class=\"aihm-dl-node aihm-dl-node--feedback\" data-dl-step=\"5\">feedback<\/div>\n\t<\/div>\n\t        <\/div>\n    <\/section>\n    \t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-text\">\n\t\t\t\t\t\t\t<p class=\"ns-aih-slide-body\">The previous decade was the era where all previous efforts in making computers act &quot;intelligently&quot; came together, and where many breakthroughs shifted public attention towards the term &quot;Artificial Intelligence&quot; again, after it had been rarely used since the 1970s. By 2010, normal desktop and laptop computers were strong enough to perform AI tasks.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<section class=\"ns-aih-slide\" data-index=\"7\" aria-hidden=\"true\">\n\t\t\t\t\t\t<h3 class=\"ns-aih-slide-title\">Listening and Speaking<\/h3>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-scene\">\n\t\t\t\t\t\t\t    <section\n        class=\"aihm-scene aihm-scene--language\"\n        id=\"ns-aih-LjOtpWw-7-language\"\n        data-aihm-scene=\"language\"\n        data-autoplay=\"1\"\n        style=\"--aihm-height:260px\"\n    >\n        <div class=\"aihm-frame\">\n            \t<div class=\"aihm-visual aihm-nlp-preview\" role=\"img\" aria-label=\"Animated natural-language-processing diagram adapted from the 9Senses NLP explainer: input becomes tokens, vectors and context, then output is checked\">\n\t\t<canvas class=\"aihm-nlp-canvas\" aria-hidden=\"true\"><\/canvas>\n\t\t<div class=\"aihm-nlp-tokenline\" aria-hidden=\"true\">\n\t\t\t<span>Can<\/span><span>we<\/span><span>ship<\/span><span>?<\/span>\n\t\t<\/div>\n\t\t<div class=\"aihm-nlp-node aihm-nlp-node--input\" data-nlp-step=\"0\">input<\/div>\n\t\t<div class=\"aihm-nlp-node aihm-nlp-node--tokens\" data-nlp-step=\"1\">tokens<\/div>\n\t\t<div class=\"aihm-nlp-node aihm-nlp-node--vectors\" data-nlp-step=\"2\">vectors<\/div>\n\t\t<div class=\"aihm-nlp-core\" data-nlp-step=\"3\">context<\/div>\n\t\t<div class=\"aihm-nlp-node aihm-nlp-node--output\" data-nlp-step=\"4\">output<\/div>\n\t\t<div class=\"aihm-nlp-node aihm-nlp-node--check\" data-nlp-step=\"5\">check<\/div>\n\t\t<div class=\"aihm-nlp-outputline\" aria-hidden=\"true\"><span>yes<\/span><span>\u2014<\/span><span>with<\/span><span>review<\/span><\/div>\n\t\t<div class=\"aihm-nlp-warning\" data-nlp-step=\"5\" aria-hidden=\"true\">confidence <b>\u2260<\/b> correctness<\/div>\n\t<\/div>\n\t        <\/div>\n    <\/section>\n    \t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"ns-aih-slide-text\">\n\t\t\t\t\t\t\t<p class=\"ns-aih-slide-body\">Finally, conversational AI is able to have conversations with humans based on Large Language Models that have been released. Those models are routinely able to pass the Turing Test, which means that they are able to understand and communicate back in natural language.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t<\/div>\n\t<\/div>\n\t<\/div><\/div><\/div><\/div><\/div><div class=\"et_pb_section_3 et_pb_section et_section_regular et_block_section preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_row_4 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_5 et_pb_column et_pb_column_2_3 et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_5 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>But what is AI really? We have asked two conversational AI systems about their definition of Artificial Intelligence and they came back with quite divergent answers. <a href=\"#AIonAI\">Click to see what AI has to say on AI<\/a><\/p>\n<\/div><\/div><div class=\"et_pb_text_6 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>With two differing statements from two AI systems, we are not afraid of creating our own answer. We at 9senses define AI as<strong> \"a computer system that is able to react to an event it has never experienced before in a meaningful way that is adequate to that event, based on the analysis of many similar events from data.\"<\/strong> This ability clearly distinguishes it from traditional computer logic where each event (or combination of events) has only one defined reaction. We explicitly stay away from comparing it with humans, because in some areas, computers <a href=\"\/why-9\">are still eons away from reaching our abilities, while in others, they massively outperform us<\/a>.<\/p>\n<\/div><\/div><\/div><div class=\"et_pb_column_6 et_pb_column et_pb_column_1_3 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_module et_pb_button_module_wrapper et_pb_button_0_wrapper preset--group--divi-button--divi-box-shadow--default_wrapper preset--group--divi-button--divi-box-shadow--h1j7zeq--default_wrapper\"><a class=\"et_pb_button_0 et_pb_button et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-button--divi-box-shadow--default preset--group--divi-button--divi-box-shadow--h1j7zeq--default\" href=\"#AIonAI\" style=\"text-wrap:balance\">what AI says about AI<\/a><\/div><\/div><\/div><\/div><div class=\"et_pb_section_4 et_pb_section et_section_regular et_block_section ns-block ns-tabset preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\" lang=\"en\" style=\"max-width:1080px;margin-left:auto;margin-right:auto;border-radius:0;hyphens:auto;-webkit-hyphens:auto;-ms-hyphens:auto\" id=\"Key_fields_of_AI\"><div class=\"et_pb_row_5 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_7 et_pb_column et_pb_column_4_4 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_7 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2 style=\"display: inline; margin: 0 0.6em 0 0; padding: 0;\">Key Fields of AI<\/h2>\n<p><span>There are various key AI technology areas, here is one of many ways to break them down:<\/span><\/p>\n<\/div><\/div><div class=\"et_pb_code_2 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\"><style>#cv .et_pb_group_carousel_arrow{background:none!important;box-shadow:none!important;border:0!important;border-radius:0!important;color:#B3B3B3!important;font-size:25px!important;width:26px!important;height:26px!important;padding:0!important;transform:translateY(-50%)!important;opacity:.85;transition:opacity .3s ease,color .3s ease}#cv .et_pb_group_carousel_arrow:hover{opacity:1;color:#fff!important}#cv .et_pb_group_carousel_arrow_prev{left:-34px!important;right:auto!important;margin:0!important;transform:translateY(-50%) translateX(0)!important}#cv 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.et_pb_group_carousel_container{overflow:hidden!important}#cv .et_pb_group_carousel_arrow{top:120px!important;transform:translateY(-50%)!important}#cv .et_pb_group_carousel_slide>.et_pb_group{display:flex!important;flex-direction:column!important;justify-content:flex-start!important}#cv .et_pb_group_carousel_slide>.et_pb_group>*{flex:0 0 auto!important}#cv .et_pb_group_carousel h2{margin:14px 18px 0!important}#cv .et_pb_group_carousel p,#cv .et_pb_group_carousel p span,#cv .et_pb_group_carousel .et_pb_text,#cv .et_pb_group_carousel .et_pb_text *{color:rgba(255,255,255,.8)!important;font-weight:500!important}#cv .et_pb_group_carousel p{margin:10px 18px 0!important;font-size:clamp(14px,1.15vw,16px)!important}#cv .et_pb_group_carousel h2,#cv .et_pb_group_carousel h2 span{color:#58A7F9!important}#cv .et_pb_group_carousel_slide>.et_pb_group{margin:0!important;width:100%!important;max-width:none!important}#cv .et_pb_group_carousel_slide{padding:0 12px!important;box-sizing:border-box!important}#cv .et_pb_group_carousel span,#cv .et_pb_group_carousel .et_pb_text_inner span{background:none!important;background-color:transparent!important}#cv .et_pb_group_carousel_slide>.et_pb_group{padding:0 0 22px!important}#cv .et_pb_group_carousel_slide>.et_pb_group{gap:0!important;row-gap:0!important}#cv .et_pb_group_carousel_slide .et_pb_text,#cv .et_pb_group_carousel_slide .et_pb_image{margin:0!important;padding:0!important}#cv .et_pb_group_carousel h2{margin:22px 18px 0!important}#cv .et_pb_group_carousel p{margin:8px 18px 0!important}#cv .et_pb_group_carousel .et_pb_text h2,#cv .et_pb_group_carousel .et_pb_text h2 span{color:#58A7F9!important}#cv .et_pb_group_carousel_slide .et_pb_image{margin:0!important;width:100%!important;max-width:none!important}#cv .et_pb_group_carousel_slide .et_pb_image img{width:100%!important}#cv .et_pb_group_carousel_arrow_prev{left:-28px!important}#cv .et_pb_group_carousel_arrow_next{right:-22px!important}#cv .et_pb_group_carousel_slide{flex:0 0 25%!important;width:25%!important;max-width:25%!important;padding:0 24px 0 0!important;box-sizing:border-box!important}#cv .et_pb_group_carousel_track{gap:0!important}#cv .et_pb_group_carousel{width:calc(100% + 56px)!important;margin-left:-18px!important}#cv .et_pb_group_carousel_arrow{top:50%!important}#cv .et_pb_group_carousel_arrow{top:76px!important}#cv .et_pb_group_carousel .et_pb_text h2{font-size:clamp(18px,1.6vw,22px)!important}#cv .et_pb_group_carousel .et_pb_text h2{font-size:clamp(18px,1.6vw,22px)!important}<\/style><\/div><\/div><\/div><\/div><div class=\"et_pb_row_6 et_pb_row et_pb_row_4col et_pb_gutters2 et_block_row et_block_row_4col preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_8 et_pb_column et_pb_column_1_4 et_flex_column et_pb_css_mix_blend_mode_passthrough hovergroup preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default preset--module--divi-column--47yw920uxm\" data-interaction-trigger=\"pck67o4xxc\" id=\"trigger-ml\"><div class=\"et_pb_blurb_0 et_pb_blurb et_pb_bg_layout_light et_pb_blurb_position_top et_pb_module et_block_module preset--group--divi-blurb--divi-box-shadow--default preset--group--divi-blurb--divi-font-body--h19rs5u--7p5s44libg preset--group--divi-blurb--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_blurb_content\"><div class=\"et_pb_blurb_container\"><h3 class=\"et_pb_module_header\">Ma\u00adchine Learn\u00ading<\/h3><div class=\"et_pb_blurb_description\"><p style=\"text-align: justify;\"><span>Finding patterns in large datasets and drawing con\u00ad\u00ad\u00adclusions is at the core of most AI applications these days.\u00a0 Machine Lear\u00adn\u00ading provides the statistical methods to make it happen.<\/span><\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_icon_0 et_pb_icon et_pb_module et_flex_module preset--group--divi-icon--divi-box-shadow--default preset--module--divi-icon--tzlxoly2w0\"><span class=\"et_pb_icon_wrap\"><span class=\"et-pb-icon\">3<\/span><\/span><\/div><\/div><div class=\"et_pb_column_9 et_pb_column et_pb_column_1_4 et_flex_column et_pb_css_mix_blend_mode_passthrough hovergroup preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default preset--module--divi-column--47yw920uxm\" data-interaction-trigger=\"pck67o4xxc\" id=\"trigger-nlp\"><div class=\"et_pb_blurb_1 et_pb_blurb et_pb_bg_layout_light et_pb_blurb_position_top et_pb_module et_block_module preset--group--divi-blurb--divi-box-shadow--default preset--group--divi-blurb--divi-font-body--h19rs5u--7p5s44libg preset--group--divi-blurb--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_blurb_content\"><div class=\"et_pb_blurb_container\"><h3 class=\"et_pb_module_header\">Nat\u00adural Lan\u00adguage Pro\u00adcess\u00ading<\/h3><div class=\"et_pb_blurb_description\"><p style=\"text-align: justify;\">Being able to communicate with humans is one of the most recent key AI develop\u00adments that helps interact with computers, for example in customer-facing IT.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_icon_1 et_pb_icon et_pb_module et_flex_module preset--group--divi-icon--divi-box-shadow--default preset--module--divi-icon--tzlxoly2w0\"><span class=\"et_pb_icon_wrap\"><span class=\"et-pb-icon\">3<\/span><\/span><\/div><\/div><div class=\"et_pb_column_10 et_pb_column et_pb_column_1_4 et_flex_column et_pb_css_mix_blend_mode_passthrough hovergroup preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default preset--module--divi-column--47yw920uxm\" data-interaction-trigger=\"pck67o4xxc\" id=\"trigger-cv\"><div class=\"et_pb_blurb_2 et_pb_blurb et_pb_bg_layout_light et_pb_blurb_position_top et_pb_module et_block_module preset--group--divi-blurb--divi-box-shadow--default preset--group--divi-blurb--divi-font-body--h19rs5u--7p5s44libg preset--group--divi-blurb--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_blurb_content\"><div class=\"et_pb_blurb_container\"><h3 class=\"et_pb_module_header\">Com\u00adputer Vi\u00adsion<\/h3><div class=\"et_pb_blurb_description\"><p style=\"text-align: justify;\">Finding items and differences in still or moving imagery is something that computers excel at, for example when it comes to surveillance, irre\u00adgularity detection or simply - counting.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_icon_2 et_pb_icon et_pb_module et_flex_module preset--group--divi-icon--divi-box-shadow--default preset--module--divi-icon--tzlxoly2w0\"><span class=\"et_pb_icon_wrap\"><span class=\"et-pb-icon\">3<\/span><\/span><\/div><\/div><div class=\"et_pb_column_11 et_pb_column et_pb_column_1_4 et-last-child et_flex_column et_pb_css_mix_blend_mode_passthrough hovergroup preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default preset--module--divi-column--47yw920uxm\" data-interaction-trigger=\"pck67o4xxc\" id=\"trigger-robotics\"><div class=\"et_pb_blurb_3 et_pb_blurb et_pb_bg_layout_light et_pb_blurb_position_top et_pb_module et_block_module preset--group--divi-blurb--divi-box-shadow--default preset--group--divi-blurb--divi-font-body--h19rs5u--7p5s44libg preset--group--divi-blurb--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_blurb_content\"><div class=\"et_pb_blurb_container\"><h3 class=\"et_pb_module_header\">Ro\u00adbot\u00adics<\/h3><div class=\"et_pb_blurb_description\"><p style=\"text-align: justify;\">Creating autonomous sys\u00adtems that perform phy\u00adsical actions, like driving a vehicle based on controlling equipment using sensor in\u00adput and logic, is a key field of AI, albeit a difficult one.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_icon_3 et_pb_icon et_pb_module et_flex_module preset--group--divi-icon--divi-box-shadow--default preset--module--divi-icon--tzlxoly2w0\"><span class=\"et_pb_icon_wrap\"><span class=\"et-pb-icon\">3<\/span><\/span><\/div><\/div><\/div><\/div><div class=\"et_pb_section_5 et_pb_section et_section_regular et_flex_section ns-panel preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\" id=\"ml\"><div class=\"et_pb_row_7 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_12 et_pb_column et_pb_column_2_3 et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_8 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--group--divi-text--divi-font-body--h1yjkjr--7p5s44libg preset--module--divi-text--0431a145-bb8b-4440-b3c5-0a16c179fb90\"><div class=\"et_pb_text_inner\"><p>Whichever AI field you look at, whether NLP, Computer Vision or Robotics, it is usually Machine Learning doing the actual work underneath: models learn from historical data and apply what they have learned to data they have never seen before. Or, as Wikipedia puts it: \"Machine Learning is a field of study in artificial intelligence concerned with the development of statistical algorithms that can learn from data and generalize to unseen data; and thus perform tasks without explicit instructions.\"<\/p>\n<\/div><\/div><div class=\"et_pb_code_3 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\">\t<div\n\t\tid=\"ns-aix-1\"\n\t\tclass=\"ns-aix ns-aix-ml\"\n\t\tdata-topic=\"ml\"\n\t\tdata-flow=\"core\"\n\t\t\t\tdata-step=\"0\"\n\t\tdata-interval=\"9000\"\n\t\t\t\tdata-autoplay=\"1\"\n\t\trole=\"region\"\n\t\taria-label=\"How machine learning works\"\n\t\tstyle=\"--ns-aix-accent:#58a7f9;--ns-aix-secondary:#2b6cb0;--ns-aix-ink:#D6D6D6\"\n\t>\n\t\t<header class=\"ns-aix-head\">\n\t\t\t\t\t\t<h3 class=\"ns-aix-title\">How machine learning works<\/h3>\n\t\t\t<p class=\"ns-aix-intro\">Machine learning is not a static flow of logical routines. It is a system that finds useful patterns in examples, turns those patterns into a model and applies the model to new situations.<\/p>\n\t\t\t\t\t\t\t\t<\/header>\n\n\t\t<div class=\"ns-aix-body\">\n\t\t\t<div class=\"ns-aix-copy\">\n\t\t\t\t<div class=\"ns-aix-progress\" aria-hidden=\"true\"><span><\/span><\/div>\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\tid=\"ns-aix-1-flow-panel-core\"\n\t\t\t\t\t\tclass=\"ns-aix-step-panel ns-on\"\n\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t<ol class=\"ns-aix-steps ns-on\" data-flow=\"core\" role=\"list\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step ns-on\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\t\tdata-key=\"observe\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"true\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-0\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">01<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Observe<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Raw cases enter the system.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\t\tdata-key=\"encode\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-1\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">02<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Encode<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Meaning becomes geometry.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\t\tdata-key=\"train\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-2\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">03<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Train<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Predict, measure error, adjust.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\t\tdata-key=\"generalize\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-3\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">04<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Generalize<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Structure over memorization.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\t\tdata-key=\"infer\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-4\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">05<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Infer<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">New input becomes a decision aid.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\t\tdata-key=\"validate\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-5\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">06<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Validate<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">The model must be tested outside training.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\t\tdata-key=\"improve\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-6\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">07<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Improve<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Feedback turns deployment into learning.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<\/ol>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t<div class=\"ns-aix-stage\" aria-label=\"Animated vector-map process diagram\">\n\t\t\t\t<canvas class=\"ns-aix-canvas\" aria-hidden=\"true\"><\/canvas>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-on\" data-flow=\"core\" data-step=\"0\" data-key=\"observe\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>data<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"1\" data-key=\"encode\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>vectors<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"2\" data-key=\"train\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>model<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"3\" data-key=\"generalize\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>structure<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"4\" data-key=\"infer\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>output<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"5\" data-key=\"validate\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>check<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"6\" data-key=\"improve\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>feedback<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<\/div>\n\n\t\t<div class=\"ns-aix-controls\">\n\t\t\t<div class=\"ns-aix-dots\" role=\"navigation\" aria-label=\"Step navigation\">\n\t\t\t\t\t\t\t\t\t<span\n\t\t\t\t\t\tclass=\"ns-aix-dotset ns-on\"\n\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\trole=\"group\"\n\t\t\t\t\t\taria-label=\"Steps for How machine learning works\"\n\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot ns-on\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\taria-label=\"Step 1: Observe\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-0\"\n\t\t\t\t\t\t\t\taria-pressed=\"true\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\taria-label=\"Step 2: Encode\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-1\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\taria-label=\"Step 3: Train\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-2\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\taria-label=\"Step 4: Generalize\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-3\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\taria-label=\"Step 5: Infer\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-4\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\taria-label=\"Step 6: Validate\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-5\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\taria-label=\"Step 7: Improve\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-1-panel-core-6\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\n\t\t<div class=\"ns-aix-below\" aria-live=\"polite\">\n\t\t\t<div class=\"ns-aix-explain\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-1-panel-core-0\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel ns-on\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">01<\/span>Start with examples<\/h4>\n\t\t\t\t\t\t\t<p>Machine learning starts with examples: documents, cases, sensor values, customer conversations or business events. The decisive question is not quantity alone, but whether the data represents the decisions the model should later support \u2014 and whether it is worth the cost of collecting and cleaning it.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-1-panel-core-1\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">02<\/span>Turn signals into vectors<\/h4>\n\t\t\t\t\t\t\t<p>Text, images and numbers are converted into numerical features or vectors: coordinates that preserve useful patterns such as similarity, frequency, intent, context or risk. This is why a model can compare things that are not literally identical.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-1-panel-core-2\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">03<\/span>Adjust the model<\/h4>\n\t\t\t\t\t\t\t<p>The model makes a prediction, compares it with the expected answer, measures the error and adjusts internal weights. Some models learn this way from labeled examples; others find structure without labels, and large language models largely teach themselves from raw text. Repeating the process turns individual examples into a reusable pattern.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-1-panel-core-3\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">04<\/span>Learn structure, not a list<\/h4>\n\t\t\t\t\t\t\t<p>A useful model does not simply memorize its training cases \u2014 that failure is called overfitting. It learns enough structure to perform well on new cases that are similar in substance, even when they are not identical in wording, format or context.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-1-panel-core-4\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">05<\/span>Apply the trained model<\/h4>\n\t\t\t\t\t\t\t<p>During inference, fresh input passes through the trained model. The output may be a score, classification, recommendation, forecast, generated answer or ranked set of documents.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-1-panel-core-5\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">06<\/span>Control quality and risk<\/h4>\n\t\t\t\t\t\t\t<p>Separate test data, human review and monitoring expose weak edge cases, bias, hallucinations, drift and compliance risks before automation affects customers, staff or regulated decisions.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-1-panel-core-6\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">07<\/span>Close the feedback loop<\/h4>\n\t\t\t\t\t\t\t<p>Real outcomes and corrections flow back into the system. Handled carelessly this can entrench old bias, so the model, retrieval layer, prompts, data pipeline and governance rules are improved deliberately instead of being left to drift.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<div class=\"ns-aix-aspects\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset ns-on\" data-flow=\"core\" data-step=\"0\" aria-hidden=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Supervised learning works from labeled examples; unsupervised learning finds structure in unlabeled data; reinforcement learning improves by trial and error through feedback.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>To predict machine failures, operational data is matched with recorded failures so the system has examples to learn from.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Machine learning is primarily pattern recognition in mostly large datasets. Encoding is what makes those patterns measurable.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Vector representations let search, recommendation and retrieval systems compare things that are similar in substance rather than identical in form.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"2\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Background<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Machine learning goes back to the 1950s, when researchers built algorithms that could recognize simple patterns in data. Big data, powerful hardware \u2014 particularly GPUs \u2014 and improved algorithms drove the breakthroughs of the early 21st century.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>During training the algorithm makes predictions and compares them with known outcomes. Repeated many times, this gradually improves accuracy.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"3\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Modern machine learning often relies on artificial neural networks \u2014 mathematical systems loosely inspired by the brain that improve by learning from experience.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The finished model must work on new, unseen data of the same kind, not merely reproduce the cases it was trained on.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"4\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>What ML can do<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Machine learning powers search engines, recommendation systems, voice assistants, fraud detection, image analysis and autonomous systems \u2014 in many narrow tasks faster and more accurately than humans.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>It can identify tumors in scans, predict equipment failures, translate languages and generate text, images and code.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"5\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Because a model does not understand context on an abstract level, it needs clear guidance during training and review, with boundaries set by humans.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>An opaque model can become a black box where it stays unclear which patterns drive its decisions. Careful governance is essential wherever the impact is high.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"6\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>What ML can do<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>ML is strongest where large amounts of structured or unstructured data hide patterns too subtle for manual analysis: drug discovery and climate modeling in research; forecasting, segmentation and process optimization in business.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Outcomes depend heavily on data quality \u2014 bad data leads to incorrect results, and unmanaged feedback loops quietly reinforce yesterday&#039;s bias.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\t<\/div><\/div><\/div><div class=\"et_pb_column_13 et_pb_column et_pb_column_1_3 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_9 et_pb_text et_pb_bg_layout_light et_pb_module et_flex_module preset--group--divi-text--divi-box-shadow--default preset--group--divi-text--divi-font-body--h1yjkjr--7p5s44libg preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2>The Challenges<\/h2>\n<p style=\"text-align: justify;\">Machine Learning has important limitations. It heavily depends on the quality and quantity of the underlying data to find the relevant statistical patterns. Bad data leads to incorrect outcomes.<\/p>\n<p style=\"text-align: justify;\">As machine learning - like all \"AI\" - doesn't really understand the context on an abstract level, it needs clear guidance during training and reviews, often coupled with additional boundaries set by humans.<\/p>\n<p style=\"text-align: justify;\">This becomes particularly difficult when the model itself is intransparent and becomes a \"black box\", where it remains unclear what patterns drive decisions made by ML. Thus, careful governance is essential, particularly in areas with high impact.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_8 et_pb_row et_flex_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_14 et_pb_column et-last-child et_flex_column et_pb_css_mix_blend_mode_passthrough et_flex_column_24_24 et_flex_column_24_24_tablet et_flex_column_24_24_phone et_flex_column_24_24_tabletWide preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_code_4 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\"><div class=\"nsp-frame nsp-frame--transparent\"><div class=\"nsp-frame-head\"><h2>Featured Projects<\/h2><\/div><div class=\"nsp-carousel-wrap\" data-base-width=\"240\" data-loop=\"1\"><button type=\"button\" class=\"nsp-nav nsp-prev\" aria-label=\"Previous\"><span class=\"nsp-nav-icon et-pb-icon\" aria-hidden=\"true\">&#x34;<\/span><\/button><div class=\"nsp-carousel\">\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"12\" aria-label=\"Intelligent Data Retrieval Agent\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><span class=\"nsp-card-icon et-pb-icon\" aria-hidden=\"true\">&#xe0f7;<\/span><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Intelligent Data Retrieval Agent<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Production-ready AI retrieval system using LLMs and semantic search to transform fragmented data into reliable, searchable knowledge.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"13\" aria-label=\"Visual Search Recommendations\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-socks\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Visual Search Recommendations<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">eCommmerce plugin that enables searching for visually similar products, helping customers to find and compare multiple related items.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"14\" aria-label=\"Visual Assistance for Seniors\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-glasses\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Visual Assistance for Seniors<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Visual Assistance App: enabling visual assistance for seniors by helping position determination using Computer Vision and Deep Learning.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"1\" aria-label=\"Electrical Switch Monitor\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-toggle-on\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Electrical Switch Monitor<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Public transportation: using AI-driven vision to monitor old-fashioned electrical relays and also to evaluate potential failures for predictive maintenance.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"5\" aria-label=\"Hydropower Plant Operations\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-water\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Hydropower Plant Operations<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Create a control and monitoring solution for all plant operations, including predictive maintenance logic and intrusion mon\u00aditoring.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"2\" aria-label=\"Customer Interaction Analysis\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><span class=\"nsp-card-icon et-pb-icon\" aria-hidden=\"true\">&#x77;<\/span><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Customer Interaction Analysis<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">SaaS project platform: the objective was to evaluate dialogue quality using an AI model to ensure timely intervention and customer care.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"6\" aria-label=\"Motion-sensitive wearables\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-shirt\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Motion-sensitive wearables<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Wearable fabric-based devices with embedded microcontrollers and sensitivity for motion, heartbeat, body temperature and sweat detection.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t<\/div><button type=\"button\" class=\"nsp-nav nsp-next\" aria-label=\"Next\"><span class=\"nsp-nav-icon et-pb-icon\" aria-hidden=\"true\">&#x35;<\/span><\/button><\/div><script>(function(){\n\t\t\tif (!window.nspComputeLayout) {\n\t\t\t\twindow.NSP_BASE_CARD_WIDTH = 240;\n\t\t\t\twindow.nspIsFluidWidth = function(){ return window.innerWidth > 640; };\n\t\t\t\twindow.nspComputeLayout = function(available, gap, totalCards, baseWidth){\n\t\t\t\t\tvar BASE = baseWidth || window.NSP_BASE_CARD_WIDTH;\n\t\t\t\t\tavailable = Math.max(0, available || 0);\n\t\t\t\t\tgap = Math.max(0, gap || 0);\n\t\t\t\t\ttotalCards = Math.max(0, totalCards || 0);\n\t\t\t\t\tif (!totalCards){\n\t\t\t\t\t\treturn { count: 0, width: BASE, center: true };\n\t\t\t\t\t}\n\t\t\t\t\tvar per = BASE + gap;\n\t\t\t\t\tvar baseCount = Math.max(1, Math.floor((available + gap) \/ per));\n\t\t\t\t\tvar used = baseCount * BASE + (baseCount - 1) * gap;\n\t\t\t\t\tvar remainder = available - used;\n\t\t\t\t\t\/\/ Only create an extra visible slot when there is actually another card to fill it.\n\t\t\t\t\t\/\/ If exactly the base-width row count is present, distribute those cards evenly\n\t\t\t\t\t\/\/ across the row instead of leaving them beside a phantom extra slot.\n\t\t\t\t\tvar fullCount = (totalCards > baseCount && remainder >= (2 \/ 3) * BASE) ? baseCount + 1 : baseCount;\n\t\t\t\t\tfullCount = Math.max(1, fullCount);\n\t\t\t\t\tvar fullWidth = (available - (fullCount - 1) * gap) \/ fullCount;\n\t\t\t\t\tif (!isFinite(fullWidth) || fullWidth <= 0){ fullWidth = BASE; }\n\n\t\t\t\t\t\/\/ One or two cards should never grow just because the row is wide.\n\t\t\t\t\t\/\/ They keep the base width, sit at the left, and leave the rest of the row empty.\n\t\t\t\t\tif (totalCards < 3){\n\t\t\t\t\t\tvar smallMax = (available - (totalCards - 1) * gap) \/ totalCards;\n\t\t\t\t\t\tvar smallWidth = (isFinite(smallMax) && smallMax > 0) ? Math.min(BASE, smallMax) : BASE;\n\t\t\t\t\t\treturn { count: totalCards, width: smallWidth, center: false };\n\t\t\t\t\t}\n\n\t\t\t\t\t\/\/ If there are fewer cards than a full row would hold, keep the full-row\n\t\t\t\t\t\/\/ card width rather than stretching those few across the whole row, and\n\t\t\t\t\t\/\/ leave the remaining slots empty. Left-aligned, as the master grid is.\n\t\t\t\t\tif (totalCards < fullCount){\n\t\t\t\t\t\treturn { count: totalCards, width: fullWidth, center: false };\n\t\t\t\t\t}\n\n\t\t\t\t\tvar count = Math.min(fullCount, totalCards);\n\t\t\t\t\treturn { count: count, width: fullWidth, center: false };\n\t\t\t\t};\n\t\t\t\t\/\/ Centre the chevrons on the media block of a card rather than on\n\t\t\t\t\/\/ the whole card: on people cards that is the square photo\n\t\t\t\t\/\/ (whose height tracks the fluid card width, so it cannot be\n\t\t\t\t\/\/ expressed in CSS), on project cards the icon block. Published\n\t\t\t\t\/\/ as a custom property the stylesheet reads.\n\t\t\t\twindow.nspTrackPadX = function(track){\n\t\t\t\t\tvar cs = window.getComputedStyle(track);\n\t\t\t\t\treturn (parseFloat(cs.paddingLeft) || 0) + (parseFloat(cs.paddingRight) || 0);\n\t\t\t\t};\n\t\t\t\twindow.nspSyncNav = function(wrap){\n\t\t\t\t\t\/\/ People cards anchor on the photo, which is the visual mass of\n\t\t\t\t\t\/\/ the card. Project cards have no photo -- only a small icon\n\t\t\t\t\t\/\/ at the top -- so they anchor on the card panel itself and\n\t\t\t\t\t\/\/ the arrows land on its vertical centre. Measuring against\n\t\t\t\t\t\/\/ the wrap in viewport coordinates absorbs the track padding\n\t\t\t\t\t\/\/ and card margins without restating any of them here.\n\t\t\t\t\tvar media = wrap.querySelector(\".nsp-card-photo\")\n\t\t\t\t\t\t|| wrap.querySelector(\".nsp-card\");\n\t\t\t\t\tif (!media){ return; }\n\t\t\t\t\tvar r = media.getBoundingClientRect();\n\t\t\t\t\tif (!r.height){ return; }\n\t\t\t\t\tvar w = wrap.getBoundingClientRect();\n\t\t\t\t\twrap.style.setProperty(\"--nsp-nav-top\", ((r.top - w.top) + r.height \/ 2) + \"px\");\n\t\t\t\t};\n\t\t\t}\n\t\t\tvar wrap = document.currentScript.previousElementSibling;\n\t\t\tif(!wrap){ return; }\n\t\t\t\/\/ The frame carries the visible panel, so it has to be revealed\n\t\t\t\/\/ together with the wrap -- otherwise an empty framed box paints\n\t\t\t\/\/ before the cards have been sized into it.\n\t\t\tfunction reveal(w){\n\t\t\t\tw.style.visibility = \"visible\";\n\t\t\t\tvar f = w.parentNode;\n\t\t\t\tif (f && f.classList && f.classList.contains(\"nsp-frame\")){ f.style.visibility = \"visible\"; }\n\t\t\t}\n\t\t\tvar baseWidth = parseInt(wrap.getAttribute(\"data-base-width\"), 10) || window.NSP_BASE_CARD_WIDTH;\n\t\t\tvar track = wrap.querySelector(\".nsp-carousel\");\n\t\t\tvar cards = track ? track.querySelectorAll(\".nsp-card\") : [];\n\t\t\tif(!track || !cards.length){ if (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap); return; }\n\t\t\t\/\/ Read from the stylesheet rather than restating it: a hard-coded copy\n\t\t\t\/\/ here is exactly how the gutter drifted from the CSS before.\n\t\t\tvar gap = parseFloat(window.getComputedStyle(track).columnGap) || 24;\n\t\t\tif (!window.nspIsFluidWidth()) {\n\t\t\t\t\/\/ Mobile: unchanged -- fixed per-card width (CSS 82vw), just avoid a partial\n\t\t\t\t\/\/ card peeking out at the wrap edge.\n\t\t\t\tvar cardW = cards[0].getBoundingClientRect().width;\n\t\t\t\tif(!cardW){ if (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap); return; }\n\t\t\t\t\/\/ clientWidth is the PADDING box, so the track padding has to come\n\t\t\t\t\/\/ off before this is the room the cards actually get.\n\t\t\t\tvar available0 = track.clientWidth - window.nspTrackPadX(track);\n\t\t\t\tvar fitCount = Math.max(1, Math.floor((available0 + gap) \/ (cardW + gap)));\n\t\t\t\tvar count0 = Math.min(fitCount, cards.length);\n\t\t\t\tvar needed0 = count0 * cardW + (count0 - 1) * gap;\n\t\t\t\tvar ws0 = window.getComputedStyle(wrap);\n\t\t\t\tvar padX0 = (parseFloat(ws0.paddingLeft) || 0) + (parseFloat(ws0.paddingRight) || 0);\n\t\t\t\tvar target0 = needed0 + window.nspTrackPadX(track) + (ws0.boxSizing === \"border-box\" ? padX0 : 0);\n\t\t\t\twrap.style.maxWidth = Math.ceil(target0) + \"px\";\n\t\t\t\tif (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\t\/\/ Desktop\/tablet: fluid card width, filling the row exactly either way.\n\t\t\t\/\/ clientWidth is the PADDING box -- take the track padding off it.\n\t\t\tvar available = track.clientWidth - window.nspTrackPadX(track);\n\t\t\tvar layout = window.nspComputeLayout(available, gap, cards.length, baseWidth);\n\t\t\tfor (var i = 0; i < cards.length; i++){ cards[i].style.flex = \"0 0 \" + layout.width + \"px\"; }\n\t\t\tif (layout.center) {\n\t\t\t\tvar totalW = layout.count * layout.width + (layout.count - 1) * gap;\n\t\t\t\twrap.style.maxWidth = Math.ceil(totalW + window.nspTrackPadX(track)) + \"px\";\n\t\t\t\twrap.style.marginLeft = \"auto\";\n\t\t\t\twrap.style.marginRight = \"auto\";\n\t\t\t}\n\t\t\tif (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap);\n\t\t})();<\/script><\/div><noscript><style>.nsp-carousel-wrap,.nsp-frame{visibility:visible !important;}<\/style><\/noscript><div class=\"nsp-popups\" hidden><template class=\"nsp-popup-tpl\" data-id=\"12\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<span class=\"nsp-pop-icon et-pb-icon\" aria-hidden=\"true\">&#xe0f7;<\/span>\t\t\t\t<span class=\"nsp-dlg-h-title\">Intelligent Data Retrieval Agent<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>Development of a production-ready retrieval agent for querying large, fragmented, and undocumented enterprise data repositories using large language models and semantic search. The solution transformed scattered legacy records into structured, searchable knowledge, enabling users to retrieve relevant information within seconds instead of manually searching across hundreds of documents.<\/p>\n<p>The project combined retrieval-augmented generation (RAG), vector search, and modern LLM technologies to deliver reliable, context-aware information retrieval. Designed with a modular architecture, the system supports scalability, maintainability, and future extensions while ensuring robust retrieval quality across heterogeneous data sources.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--below\"><p>AI Engineer<\/p>\n<p>Python \u2022 LangChain \u2022 OpenAI \u2022 Qdrant \u2022 Semantic Search \u2022 RAG \u2022 Streamlit<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/nlp\">Natural Language Processing<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/control-and-automation\">Automation<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"13\">\t\t<div class=\"nsp-dialog has-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t\t\t\t\t<span class=\"nsp-dlg-h-title\">Visual Search Recommendations<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>For VisualSearch, the focus was on developing and deploying e-commerce plugins to enhance recommendation functionalities in web shops. The plugins were successfully launched in the store and happily adopted by customers, demonstrating their practical use in improving e-commerce experiences. Key elements included:<br \/>\n- computation of visual embeddings from appearances of e-commerce products<br \/>\n- building and maintaining a search index using these embeddings<br \/>\n- providing a cloud-based API for Shopware and Prestashop plugins<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-aside\">\n\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"369\" height=\"455\" src=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/visualsearch.png\" class=\"nsp-pop-img\" alt=\"\" srcset=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/visualsearch.png 369w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/visualsearch-243x300.png 243w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/visualsearch-10x12.png 10w\" sizes=\"(max-width: 369px) 100vw, 369px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--aside\"><p>Peter acted as the project manager and key data scientist on this project, leveraging AWS services, Python, and deep learning frameworks like Keras. The project integrated cloud-based solutions using CloudFormation, Lambda, and Gateway for scalable and efficient deployment, using SQL and DynamoDB for data management.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"14\">\t\t<div class=\"nsp-dialog has-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t\t\t\t\t<span class=\"nsp-dlg-h-title\">Visual Assistance for Seniors<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>For the Austrian Institute of Technology (as a part of the national research project LARAH), a prototype of an assistance system for visual indoor localization for disabled and elderly persons was developed. By leveraging Computer Vision and Deep Learning, innovative algorithms for position determination were implemented. The project resulted in a functional prototype, including two Android applications for real-time localization. Key elements included:<br \/>\n- visual recognition of persons using Deep-Learning models<br \/>\n- visual reconstruction and localization of indoor environments using Structure-from-Motion and Machine Learning algorithms<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-aside\">\n\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"366\" height=\"451\" src=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/larah.png\" class=\"nsp-pop-img\" alt=\"\" srcset=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/larah.png 366w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/larah-243x300.png 243w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/larah-10x12.png 10w\" sizes=\"(max-width: 366px) 100vw, 366px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--aside\"><p>Peter acted as the project manager and key data scientist on this project, developed under Python using Deep Learning models like TensorFlow, custom-developed Structure-from-Motion software and custom camera calibration software. Additionally, as a part of the project, two Android apps were modified and integrated together onto the Robot Operating System on the mobile platform.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"1\">\t\t<div class=\"nsp-dialog has-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t\t\t\t\t<span class=\"nsp-dlg-h-title\">Electrical Switch Monitor<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>For a public transportation network, the objective was to optimize the monitoring of their legacy electrical switchboards. These decade-old items that are often located in very remote areas are prone to failures and tracking of errors was not possible. The objective was to enable real-time tracking and the recognition of upcoming failures from changed switching behavior. The key elements were:<\/p>\n<ul>\n<li>Development of specific hardware configuration with custom housings (3D printed) to mount instead of regular switchboard covers;<\/li>\n<li>Camera control and initial image generation on Raspberry Pi integrated in housing;<\/li>\n<li>Initial scan of switch layout and labels;<\/li>\n<li>Identification of switching operations and registration of new positions;<\/li>\n<li>Identification of irregular switching patterns (delays, other irregularities) to indicate upcoming failures for predictive maintenance;<\/li>\n<li>Update of central database and cloud solution with last state and observed switching patterns;<\/li>\n<\/ul>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-aside\">\n\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"959\" src=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/6by6cropped-1024x959.jpg\" class=\"nsp-pop-img\" alt=\"\" srcset=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/6by6cropped-980x918.jpg 980w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/6by6cropped-480x450.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) 1024px, 100vw\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--aside\"><p>The solution was implemented using Python on Raspberry Pi devices, backbone and cloud processing were done using a LAMP stack, with PyTorch, TensorFlow and OpenCV.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"5\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<i class=\"nsp-pop-icon fa-solid fa-water\" aria-hidden=\"true\"><\/i>\t\t\t\t<span class=\"nsp-dlg-h-title\">Hydropower Plant Operations<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><div class=\"et_pb_column_11 et_pb_column et_flex_column et_pb_css_mix_blend_mode_passthrough et_flex_column_6_24 et_flex_column_12_24_tablet et_flex_column_24_24_phone et_flex_column_12_24_tabletWide preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\">\n<div class=\"et_pb_blurb_2 et_pb_blurb et_pb_bg_layout_light et_pb_blurb_position_top et_pb_module et_flex_module hovergroup preset--group--divi-blurb--divi-box-shadow--default preset--group--divi-blurb--divi-font-body--h19rs5u--7p5s44libg preset--group--divi-blurb--divi-sizing--hsj9uxo--default\">\n<div class=\"et_pb_blurb_content et_flex_module\">\n<div class=\"et_pb_blurb_container\">\n<div class=\"et_pb_blurb_description\">\n<p>Create an integrated monitoring and surveillance solution for small-scale hydropower plants in remote locations. The solution included a full range of required settings:<\/p>\n<ul>\n<li>real-time monitoring and logging of operations<\/li>\n<li>failure detection and automated<\/li>\n<li>predictive maintenance logic to identify early failure<\/li>\n<li>camera-based intrusion and irregularity detection<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--below\"><p>The solution was a hybrid solution using Siemens PLCs, combined with small edge computing elements (Raspberry Pi and Arduino). All primary logic (particularly shutdown and load adjustment) was local, but key decisions and aggregations were executed online based on regular data transmission to a cloud-based management and operations suite.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/automation-and-control\">Infrastructure Management<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"2\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<span class=\"nsp-pop-icon et-pb-icon\" aria-hidden=\"true\">&#x77;<\/span>\t\t\t\t<span class=\"nsp-dlg-h-title\">Customer Interaction Analysis<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>For a project management SaaS solution where customers were matched with freelancers, a custom AI solution was established with the purpose to improve experiences for all parties. Key purposes were to create an early warning system to help customer service intervene in case of issues:<\/p>\n<ul>\n<li>Identification of unusual patterns (delays indicating inaction, intense exchanges);<\/li>\n<li>Flagging of language transgressions on both sides (use of inappropriate language, aggression);<\/li>\n<li>Matching of final ratings with evaluation of flow and dialogue quality to foster a more honest rating culture;<\/li>\n<li>Language style matching to improve future matching of freelancers to clients;<\/li>\n<\/ul>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--below\"><p>The solution was implemented using Python on a LAMP stack, with self-developed machine learning libraries.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/nlp\">Natural Language Processing<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"6\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<i class=\"nsp-pop-icon fa-solid fa-shirt\" aria-hidden=\"true\"><\/i>\t\t\t\t<span class=\"nsp-dlg-h-title\">Motion-sensitive wearables<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>Arduino-based, with edge ml functionality and periodic link via BLE to connected phone. Machine learning algorithms on cloud detecting various health states and delivering alerts to phone or web app. All logic woven into a cotton fabric wristband.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><\/div><div class=\"nsp-overlay\" id=\"nsp-overlay\" role=\"dialog\" aria-modal=\"true\"><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"et_pb_section_6 et_pb_section et_section_regular et_flex_section ns-panel preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\" id=\"nlp\"><div class=\"et_pb_row_9 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_15 et_pb_column et_pb_column_2_3 et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_10 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--group--divi-text--divi-font-body--h1yjkjr--7p5s44libg preset--module--divi-text--0431a145-bb8b-4440-b3c5-0a16c179fb90\"><div class=\"et_pb_text_inner\"><p>There is nothing more fascinating than being able to converse with computers in normal language: asking questions and receiving meaningful answers. Expected for more than half a century, this only became realistically possible a few years ago with the arrival of the first large language models. These LLMs, like ChatGPT or Gemini, have since moved from research milestone to everyday business tool, from employee support to <a href=\"https:\/\/www.9senses.ai\/customer-interaction\/\">customer interactions<\/a>.<\/p>\n<\/div><\/div><div class=\"et_pb_code_5 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\">\t<div\n\t\tid=\"ns-aix-2\"\n\t\tclass=\"ns-aix ns-aix-nlp\"\n\t\tdata-topic=\"nlp\"\n\t\tdata-flow=\"development\"\n\t\t\t\tdata-step=\"0\"\n\t\tdata-interval=\"9500\"\n\t\tdata-handoff-duration=\"7200\"\t\tdata-autoplay=\"1\"\n\t\trole=\"region\"\n\t\taria-label=\"How NLP works\"\n\t\tstyle=\"--ns-aix-accent:#58a7f9;--ns-aix-secondary:#2b6cb0;--ns-aix-ink:#D6D6D6\"\n\t>\n\t\t<header class=\"ns-aix-head\">\n\t\t\t\t\t\t<h3 class=\"ns-aix-title\">How NLP works<\/h3>\n\t\t\t<p class=\"ns-aix-intro\">Natural language processing turns text into tokens, vectors and probabilities. A model is built once through training, then runs that mechanism live on every request. The same machinery makes it useful and explains why language gaps and errors remain.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-flowgroup\">\n\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-tabs\" role=\"tablist\" aria-label=\"How NLP works\">\n\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\tid=\"ns-aix-2-tab-development\"\n\t\t\t\t\t\t\tclass=\"ns-aix-tab ns-on\"\n\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"true\"\n\t\t\t\t\t\t\taria-label=\"Show NLP development and training path\"\n\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\taria-controls=\"ns-aix-2-flow-panel-development\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\tDevelopment &amp; basic training\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\tid=\"ns-aix-2-tab-execution\"\n\t\t\t\t\t\t\tclass=\"ns-aix-tab\"\n\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"Show NLP implementation path\"\n\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\taria-controls=\"ns-aix-2-flow-panel-execution\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\tImplementation\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/header>\n\n\t\t<div class=\"ns-aix-body\">\n\t\t\t<div class=\"ns-aix-copy\">\n\t\t\t\t<div class=\"ns-aix-progress\" aria-hidden=\"true\"><span><\/span><\/div>\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\tid=\"ns-aix-2-flow-panel-development\"\n\t\t\t\t\t\tclass=\"ns-aix-step-panel ns-on\"\n\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\trole=\"tabpanel\" aria-labelledby=\"ns-aix-2-tab-development\"\t\t\t\t\t>\n\t\t\t\t\t<ol class=\"ns-aix-steps ns-on\" data-flow=\"development\" role=\"list\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step ns-on\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\t\tdata-key=\"collect\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"true\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-0\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">01<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Collect<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">The dataset defines the baseline.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\t\tdata-key=\"split\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-1\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">02<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Token rules<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Tokenization is not neutral.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\t\tdata-key=\"map\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-2\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">03<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Vector map<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Dense areas behave better.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\t\tdata-key=\"train\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-3\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">04<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Train<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Fluency is optimized statistically.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\t\tdata-key=\"adapt\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-4\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">05<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Adapt<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Good deployment needs local data.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\t\tdata-key=\"test\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-5\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">06<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Test<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Average scores hide failure clusters.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\t\tdata-key=\"govern\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-6\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">07<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Govern<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Control must be designed in.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<\/ol>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\tid=\"ns-aix-2-flow-panel-execution\"\n\t\t\t\t\t\tclass=\"ns-aix-step-panel\"\n\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\trole=\"tabpanel\" aria-labelledby=\"ns-aix-2-tab-execution\"\t\t\t\t\t>\n\t\t\t\t\t<ol class=\"ns-aix-steps\" data-flow=\"execution\" role=\"list\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\t\tdata-key=\"read\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-0\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">01<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Read<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Human wording enters the pipeline.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\t\tdata-key=\"tokenize\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-1\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">02<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Tokenize<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Words become fragments.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\t\tdata-key=\"vectorize\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-2\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">03<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Vectorize<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Meaning becomes geometry.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\t\tdata-key=\"contextualize\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-3\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">04<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Context<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Attention reshapes vectors.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\t\tdata-key=\"predict\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-4\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">05<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Predict<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Fluency is built token by token.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\t\tdata-key=\"confabulate\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-5\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">06<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Risk<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Confidence can outrun evidence.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\t\tdata-key=\"ground\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-6\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">07<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Check<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Control comes from added structure.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<\/ol>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t<div class=\"ns-aix-stage\" aria-label=\"Animated vector-map process diagram\">\n\t\t\t\t<canvas class=\"ns-aix-canvas\" aria-hidden=\"true\"><\/canvas>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-on\" data-flow=\"development\" data-step=\"0\" data-key=\"collect\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>corpus<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"development\" data-step=\"1\" data-key=\"split\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>tokens<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"development\" data-step=\"2\" data-key=\"map\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>vectors<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"development\" data-step=\"3\" data-key=\"train\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>train<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"development\" data-step=\"4\" data-key=\"adapt\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>adapt<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"development\" data-step=\"5\" data-key=\"test\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>test<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"development\" data-step=\"6\" data-key=\"govern\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>govern<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"execution\" data-step=\"0\" data-key=\"read\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>input<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"execution\" data-step=\"1\" data-key=\"tokenize\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>tokens<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"execution\" data-step=\"2\" data-key=\"vectorize\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>vectors<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"execution\" data-step=\"3\" data-key=\"contextualize\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>context<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"execution\" data-step=\"4\" data-key=\"predict\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>output<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"execution\" data-step=\"5\" data-key=\"confabulate\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>risk<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"execution\" data-step=\"6\" data-key=\"ground\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>check<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<div class=\"ns-aix-handoff-panel\" role=\"status\" aria-live=\"polite\" aria-hidden=\"true\">\n\t\t\t\t\t<div class=\"ns-aix-handoff-rail\" aria-hidden=\"true\"><\/div>\n\t\t\t\t\t<div class=\"ns-aix-handoff-inner\">\n\t\t\t\t\t\t<span class=\"ns-aix-handoff-kicker\">deploying<\/span>\n\t\t\t\t\t\t<h4>The model goes live<\/h4>\n\t\t\t\t\t\t<p>Everything in Development happens once, before launch. From here the same model runs on every message a user sends \u2014 the steps that follow repeat for each request.<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\n\t\t<div class=\"ns-aix-controls\">\n\t\t\t<div class=\"ns-aix-dots\" role=\"navigation\" aria-label=\"Step navigation\">\n\t\t\t\t\t\t\t\t\t<span\n\t\t\t\t\t\tclass=\"ns-aix-dotset ns-on\"\n\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\trole=\"group\"\n\t\t\t\t\t\taria-label=\"Steps for Development &amp; basic training\"\n\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot ns-on\"\n\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\taria-label=\"Step 1: Collect\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-0\"\n\t\t\t\t\t\t\t\taria-pressed=\"true\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\taria-label=\"Step 2: Token rules\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-1\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\taria-label=\"Step 3: Vector map\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-2\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\taria-label=\"Step 4: Train\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-3\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\taria-label=\"Step 5: Adapt\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-4\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\taria-label=\"Step 6: Test\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-5\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\taria-label=\"Step 7: Govern\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-development-6\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span\n\t\t\t\t\t\tclass=\"ns-aix-dotset\"\n\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\trole=\"group\"\n\t\t\t\t\t\taria-label=\"Steps for Implementation\"\n\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\taria-label=\"Step 1: Read\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-0\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\taria-label=\"Step 2: Tokenize\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-1\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\taria-label=\"Step 3: Vectorize\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-2\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\taria-label=\"Step 4: Context\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-3\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\taria-label=\"Step 5: Predict\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-4\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\taria-label=\"Step 6: Risk\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-5\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\taria-label=\"Step 7: Check\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-2-panel-execution-6\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\n\t\t<div class=\"ns-aix-below\" aria-live=\"polite\">\n\t\t\t<div class=\"ns-aix-explain\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-development-0\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel ns-on\"\n\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">01<\/span>Collect text<\/h4>\n\t\t\t\t\t\t\t<p>Training begins with large text collections. The model learns the statistical shape of the material it sees. Because web-scale corpora are often English-heavy, language coverage is uneven from the start unless the project deliberately compensates for it.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-development-1\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">02<\/span>Split tokens<\/h4>\n\t\t\t\t\t\t\t<p>Text is split into tokens before training, using a vocabulary that is itself learned \u2014 usually from English-heavy text. So some languages need more tokens for the same idea; compounds, inflection and sparse scripts consume context faster and weaken downstream reasoning.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-development-2\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">03<\/span>Map vectors<\/h4>\n\t\t\t\t\t\t\t<p>Tokens and passages become vectors. Frequent patterns form dense neighborhoods; rare language and niche domains form sparse zones where the nearest match may be too far away.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-development-3\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">04<\/span>Train prediction<\/h4>\n\t\t\t\t\t\t\t<p>The model repeatedly predicts hidden or next tokens and adjusts its parameters when it is wrong. Small models are cheaper and faster but have less capacity; large models capture broader patterns and languages but need more compute. Both optimize prediction, not truth.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-development-4\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">05<\/span>Adapt locally<\/h4>\n\t\t\t\t\t\t\t<p>A useful business system often needs language-specific fine-tuning, human-feedback alignment, curated examples, retrieval data and guardrails. This is extra work, especially outside English and in specialist domains.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-development-5\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">06<\/span>Test weak spots<\/h4>\n\t\t\t\t\t\t\t<p>Evaluation must probe the places where the model is most likely to fail: minority languages, specialist terminology, rare entities, ambiguous wording and questions with weak evidence.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-development-6\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"development\"\n\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">07<\/span>Govern the model<\/h4>\n\t\t\t\t\t\t\t<p>Logging, audits, refusal thresholds, human review and update cycles decide whether the result becomes a controlled system or a fluent black box with uneven language performance.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-execution-0\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">01<\/span>Receive text<\/h4>\n\t\t\t\t\t\t\t<p>A user writes a question, a document arrives, or a customer speaks to a bot. The system receives characters first, not meaning.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-execution-1\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">02<\/span>Make tokens<\/h4>\n\t\t\t\t\t\t\t<p>The text is split into words, subwords, punctuation marks or fragments. These tokens are mapped to numeric IDs before the model can process them.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-execution-2\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">03<\/span>Use vectors<\/h4>\n\t\t\t\t\t\t\t<p>Each token is represented as a high-dimensional vector: a long list of numbers. Similar contexts move tokens close together. This is proximity, not truth.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-execution-3\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">04<\/span>Apply context<\/h4>\n\t\t\t\t\t\t\t<p>Transformer attention compares each token with surrounding tokens. A word vector changes with its sentence, but the model still learns relationships rather than a grounded world model.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-execution-4\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">05<\/span>Choose output<\/h4>\n\t\t\t\t\t\t\t<p>The model estimates which token should follow next. Fluent answers emerge from repeated probability choices, but fact-checking is not part of the generation mechanism.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-execution-5\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">06<\/span>Watch risk<\/h4>\n\t\t\t\t\t\t\t<p>When the evidence is thin, the model still returns the most plausible continuation. That is why systems can sound confident and still invent facts, especially in sparse domains or weaker languages.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-2-panel-execution-6\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"execution\"\n\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">07<\/span>Ground the answer<\/h4>\n\t\t\t\t\t\t\t<p>Better systems slow the model down with retrieval, source checks, domain rules or human review. Production feedback then feeds prompt design, evaluation sets, fine-tuning and governance. Reliability is created by implementation, not by the language model alone.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<div class=\"ns-aix-aspects\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset ns-on\" data-flow=\"development\" data-step=\"0\" aria-hidden=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Background<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>NLP goes back to the 1950s and rule-based machine translation. Statistical methods took over in the 1990s and 2000s; deep learning and large language models brought the breakthrough of the 2010s.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>A model reproduces any falsehood or bias present in the material it was trained on \u2014 the corpus is destiny.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"development\" data-step=\"1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>NLP combines linguistics with machine learning: systems analyze grammar (syntax), meaning (semantics) and sometimes intent (pragmatics).<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The same sentence can cost very different token budgets across languages \u2014 a practical driver of both cost and answer quality.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"development\" data-step=\"2\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Words and subwords are mapped into a multidimensional vector space that captures patterns of meaning from how words appear together.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Hallucinations cluster where source material is thin: in sparse zones the nearest statistical neighbor can be too far away to represent a fact, yet the model still answers.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"development\" data-step=\"3\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Modern NLP relies on transformers, which process language by analyzing relationships between words in a sentence instead of rigid grammar rules.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Generation samples from probabilities, so the identical prompt can produce materially different answers across sessions \u2014 treat a single AI verdict as one draw, not a stable opinion.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"development\" data-step=\"4\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Small and large language models are first trained on general language and reasoning, then fine-tuned for individual applications and supported with specific, retrievable data.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Customer service automation, legal document review and medical record analysis all depend on this adaptation layer, not on the base model alone.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"development\" data-step=\"5\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Measured error rates rise sharply as topics become less documented \u2014 from around one percent in short-document summarization to a majority of answers on niche specialist questions.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Evaluate with domain experts on your own documents and languages, not only with public benchmarks that reward average performance.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"development\" data-step=\"6\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Human supervision and critical evaluation are essential; training and operating large models also raise privacy, misinformation and misuse concerns.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Without logging and review the system stays fluent but unaccountable \u2014 and its language performance remains uneven where nobody is measuring.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"execution\" data-step=\"0\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>What NLP can do<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Translate languages, summarize long texts, extract key information, analyze sentiment and power conversational agents \u2014 dramatically reducing manual effort in text-heavy work.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Speech recognition and speech synthesis extend the same pipeline to voice interaction with machines.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"execution\" data-step=\"1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Text never enters or leaves a language model as text. It is broken into subword fragments, each mapped to an integer ID.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Everything the model &quot;knows&quot; about your text enters through these fragments \u2014 nothing else does.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"execution\" data-step=\"2\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Each vector is typically thousands of numbers long. Training adjusts billions of parameters so tokens from similar contexts end up with mathematically similar vectors.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>At no point does the system check anything against truth \u2014 it checks proximity. Two spellings of a name are nearly identical as vectors; the statistically likelier one wins.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"execution\" data-step=\"3\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Attention lets a word&#039;s vector shift with its sentence \u2014 the reason the same word can mean different things in different contexts.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The model learns statistical relationships between words and phrases, not a grounded understanding of the world they describe.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"execution\" data-step=\"4\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The model repeatedly picks a probable next token until the answer is complete. Small statistical differences early on can cascade into a very different overall response.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Confidence is baked into the generation style and is not connected to the validity of the content \u2014 the system sounds equally certain when it is guessing.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"execution\" data-step=\"5\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Errors concentrate exactly in the topics users are least able to verify. Skepticism should therefore scale inversely with your own expertise on a subject.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Asking the same question several times in fresh sessions and comparing the answers reveals where a model is on solid ground and where it is guessing.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"execution\" data-step=\"6\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Reliability is an implementation property: retrieval, source checks, domain rules and human review are added around the model, not found inside it.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>When retrieval similarity is low, a well-built system errs toward caution: it drops the answer or explicitly flags low confidence instead of guessing.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\t<\/div><\/div><\/div><div class=\"et_pb_column_16 et_pb_column et_pb_column_1_3 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_11 et_pb_text et_pb_bg_layout_light et_pb_module et_flex_module preset--group--divi-text--divi-box-shadow--default preset--group--divi-text--divi-font-body--h1yjkjr--7p5s44libg preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2>The Challenges<\/h2>\n<p data-start=\"3434\" data-end=\"3776\">No matter how \"human\" they sound, NLP systems face important system-defined limitations. They can easily produce fluent but factually incorrect or misleading information. They also reproduce any falsehood or bias in the information available when trained.<\/p>\n<p data-start=\"3434\" data-end=\"3776\">As NLP systems are solely based on statistical patterns and have only limited contextual understanding, they can struggle with reasoning, and consistency. Also, they are solely based on the input provided during training and feedback during operations. This is particularly problematic with large open models.<\/p>\n<p data-start=\"3778\" data-end=\"4217\" data-is-last-node=\"\" data-is-only-node=\"\">Training large models requires significant resources and raises concerns about privacy, misinformation, and misuse. Human supervision and critical evaluation are thus essential.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_10 et_pb_row et_flex_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_17 et_pb_column et-last-child et_flex_column et_pb_css_mix_blend_mode_passthrough et_flex_column_24_24 et_flex_column_24_24_tablet et_flex_column_24_24_phone et_flex_column_24_24_tabletWide preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_code_6 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\"><div class=\"nsp-frame nsp-frame--transparent\"><div class=\"nsp-frame-head\"><h2>Featured Projects<\/h2><\/div><div class=\"nsp-carousel-wrap\" data-base-width=\"240\" data-loop=\"1\"><button type=\"button\" class=\"nsp-nav nsp-prev\" aria-label=\"Previous\"><span class=\"nsp-nav-icon et-pb-icon\" aria-hidden=\"true\">&#x34;<\/span><\/button><div class=\"nsp-carousel\">\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"3\" aria-label=\"RAG-driven Legal Chatbot\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-section\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">RAG-driven Legal Chatbot<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">A small-to-medium language model reliably answering German legal questions based on a strong RAG pipeline.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"7\" aria-label=\"Generative AI Audit Framework\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><span class=\"nsp-card-icon et-pb-icon\" aria-hidden=\"true\">&#xe0f7;<\/span><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Generative AI Audit Framework<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Development of a structured Generative AI audit framework. This included establishing a methodology for Level 1 and Level 2 GenAI audits<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"12\" aria-label=\"Intelligent Data Retrieval Agent\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><span class=\"nsp-card-icon et-pb-icon\" aria-hidden=\"true\">&#xe0f7;<\/span><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Intelligent Data Retrieval Agent<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Production-ready AI retrieval system using LLMs and semantic search to transform fragmented data into reliable, searchable knowledge.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"2\" aria-label=\"Customer Interaction Analysis\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><span class=\"nsp-card-icon et-pb-icon\" aria-hidden=\"true\">&#x77;<\/span><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Customer Interaction Analysis<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">SaaS project platform: the objective was to evaluate dialogue quality using an AI model to ensure timely intervention and customer care.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t<\/div><button type=\"button\" class=\"nsp-nav nsp-next\" aria-label=\"Next\"><span class=\"nsp-nav-icon et-pb-icon\" aria-hidden=\"true\">&#x35;<\/span><\/button><\/div><script>(function(){\n\t\t\tif (!window.nspComputeLayout) {\n\t\t\t\twindow.NSP_BASE_CARD_WIDTH = 240;\n\t\t\t\twindow.nspIsFluidWidth = function(){ return window.innerWidth > 640; };\n\t\t\t\twindow.nspComputeLayout = function(available, gap, totalCards, baseWidth){\n\t\t\t\t\tvar BASE = baseWidth || window.NSP_BASE_CARD_WIDTH;\n\t\t\t\t\tavailable = Math.max(0, available || 0);\n\t\t\t\t\tgap = Math.max(0, gap || 0);\n\t\t\t\t\ttotalCards = Math.max(0, totalCards || 0);\n\t\t\t\t\tif (!totalCards){\n\t\t\t\t\t\treturn { count: 0, width: BASE, center: true };\n\t\t\t\t\t}\n\t\t\t\t\tvar per = BASE + gap;\n\t\t\t\t\tvar baseCount = Math.max(1, Math.floor((available + gap) \/ per));\n\t\t\t\t\tvar used = baseCount * BASE + (baseCount - 1) * gap;\n\t\t\t\t\tvar remainder = available - used;\n\t\t\t\t\t\/\/ Only create an extra visible slot when there is actually another card to fill it.\n\t\t\t\t\t\/\/ If exactly the base-width row count is present, distribute those cards evenly\n\t\t\t\t\t\/\/ across the row instead of leaving them beside a phantom extra slot.\n\t\t\t\t\tvar fullCount = (totalCards > baseCount && remainder >= (2 \/ 3) * BASE) ? baseCount + 1 : baseCount;\n\t\t\t\t\tfullCount = Math.max(1, fullCount);\n\t\t\t\t\tvar fullWidth = (available - (fullCount - 1) * gap) \/ fullCount;\n\t\t\t\t\tif (!isFinite(fullWidth) || fullWidth <= 0){ fullWidth = BASE; }\n\n\t\t\t\t\t\/\/ One or two cards should never grow just because the row is wide.\n\t\t\t\t\t\/\/ They keep the base width, sit at the left, and leave the rest of the row empty.\n\t\t\t\t\tif (totalCards < 3){\n\t\t\t\t\t\tvar smallMax = (available - (totalCards - 1) * gap) \/ totalCards;\n\t\t\t\t\t\tvar smallWidth = (isFinite(smallMax) && smallMax > 0) ? Math.min(BASE, smallMax) : BASE;\n\t\t\t\t\t\treturn { count: totalCards, width: smallWidth, center: false };\n\t\t\t\t\t}\n\n\t\t\t\t\t\/\/ If there are fewer cards than a full row would hold, keep the full-row\n\t\t\t\t\t\/\/ card width rather than stretching those few across the whole row, and\n\t\t\t\t\t\/\/ leave the remaining slots empty. Left-aligned, as the master grid is.\n\t\t\t\t\tif (totalCards < fullCount){\n\t\t\t\t\t\treturn { count: totalCards, width: fullWidth, center: false };\n\t\t\t\t\t}\n\n\t\t\t\t\tvar count = Math.min(fullCount, totalCards);\n\t\t\t\t\treturn { count: count, width: fullWidth, center: false };\n\t\t\t\t};\n\t\t\t\t\/\/ Centre the chevrons on the media block of a card rather than on\n\t\t\t\t\/\/ the whole card: on people cards that is the square photo\n\t\t\t\t\/\/ (whose height tracks the fluid card width, so it cannot be\n\t\t\t\t\/\/ expressed in CSS), on project cards the icon block. Published\n\t\t\t\t\/\/ as a custom property the stylesheet reads.\n\t\t\t\twindow.nspTrackPadX = function(track){\n\t\t\t\t\tvar cs = window.getComputedStyle(track);\n\t\t\t\t\treturn (parseFloat(cs.paddingLeft) || 0) + (parseFloat(cs.paddingRight) || 0);\n\t\t\t\t};\n\t\t\t\twindow.nspSyncNav = function(wrap){\n\t\t\t\t\t\/\/ People cards anchor on the photo, which is the visual mass of\n\t\t\t\t\t\/\/ the card. Project cards have no photo -- only a small icon\n\t\t\t\t\t\/\/ at the top -- so they anchor on the card panel itself and\n\t\t\t\t\t\/\/ the arrows land on its vertical centre. Measuring against\n\t\t\t\t\t\/\/ the wrap in viewport coordinates absorbs the track padding\n\t\t\t\t\t\/\/ and card margins without restating any of them here.\n\t\t\t\t\tvar media = wrap.querySelector(\".nsp-card-photo\")\n\t\t\t\t\t\t|| wrap.querySelector(\".nsp-card\");\n\t\t\t\t\tif (!media){ return; }\n\t\t\t\t\tvar r = media.getBoundingClientRect();\n\t\t\t\t\tif (!r.height){ return; }\n\t\t\t\t\tvar w = wrap.getBoundingClientRect();\n\t\t\t\t\twrap.style.setProperty(\"--nsp-nav-top\", ((r.top - w.top) + r.height \/ 2) + \"px\");\n\t\t\t\t};\n\t\t\t}\n\t\t\tvar wrap = document.currentScript.previousElementSibling;\n\t\t\tif(!wrap){ return; }\n\t\t\t\/\/ The frame carries the visible panel, so it has to be revealed\n\t\t\t\/\/ together with the wrap -- otherwise an empty framed box paints\n\t\t\t\/\/ before the cards have been sized into it.\n\t\t\tfunction reveal(w){\n\t\t\t\tw.style.visibility = \"visible\";\n\t\t\t\tvar f = w.parentNode;\n\t\t\t\tif (f && f.classList && f.classList.contains(\"nsp-frame\")){ f.style.visibility = \"visible\"; }\n\t\t\t}\n\t\t\tvar baseWidth = parseInt(wrap.getAttribute(\"data-base-width\"), 10) || window.NSP_BASE_CARD_WIDTH;\n\t\t\tvar track = wrap.querySelector(\".nsp-carousel\");\n\t\t\tvar cards = track ? track.querySelectorAll(\".nsp-card\") : [];\n\t\t\tif(!track || !cards.length){ if (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap); return; }\n\t\t\t\/\/ Read from the stylesheet rather than restating it: a hard-coded copy\n\t\t\t\/\/ here is exactly how the gutter drifted from the CSS before.\n\t\t\tvar gap = parseFloat(window.getComputedStyle(track).columnGap) || 24;\n\t\t\tif (!window.nspIsFluidWidth()) {\n\t\t\t\t\/\/ Mobile: unchanged -- fixed per-card width (CSS 82vw), just avoid a partial\n\t\t\t\t\/\/ card peeking out at the wrap edge.\n\t\t\t\tvar cardW = cards[0].getBoundingClientRect().width;\n\t\t\t\tif(!cardW){ if (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap); return; }\n\t\t\t\t\/\/ clientWidth is the PADDING box, so the track padding has to come\n\t\t\t\t\/\/ off before this is the room the cards actually get.\n\t\t\t\tvar available0 = track.clientWidth - window.nspTrackPadX(track);\n\t\t\t\tvar fitCount = Math.max(1, Math.floor((available0 + gap) \/ (cardW + gap)));\n\t\t\t\tvar count0 = Math.min(fitCount, cards.length);\n\t\t\t\tvar needed0 = count0 * cardW + (count0 - 1) * gap;\n\t\t\t\tvar ws0 = window.getComputedStyle(wrap);\n\t\t\t\tvar padX0 = (parseFloat(ws0.paddingLeft) || 0) + (parseFloat(ws0.paddingRight) || 0);\n\t\t\t\tvar target0 = needed0 + window.nspTrackPadX(track) + (ws0.boxSizing === \"border-box\" ? padX0 : 0);\n\t\t\t\twrap.style.maxWidth = Math.ceil(target0) + \"px\";\n\t\t\t\tif (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\t\/\/ Desktop\/tablet: fluid card width, filling the row exactly either way.\n\t\t\t\/\/ clientWidth is the PADDING box -- take the track padding off it.\n\t\t\tvar available = track.clientWidth - window.nspTrackPadX(track);\n\t\t\tvar layout = window.nspComputeLayout(available, gap, cards.length, baseWidth);\n\t\t\tfor (var i = 0; i < cards.length; i++){ cards[i].style.flex = \"0 0 \" + layout.width + \"px\"; }\n\t\t\tif (layout.center) {\n\t\t\t\tvar totalW = layout.count * layout.width + (layout.count - 1) * gap;\n\t\t\t\twrap.style.maxWidth = Math.ceil(totalW + window.nspTrackPadX(track)) + \"px\";\n\t\t\t\twrap.style.marginLeft = \"auto\";\n\t\t\t\twrap.style.marginRight = \"auto\";\n\t\t\t}\n\t\t\tif (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap);\n\t\t})();<\/script><\/div><noscript><style>.nsp-carousel-wrap,.nsp-frame{visibility:visible !important;}<\/style><\/noscript><div class=\"nsp-popups\" hidden><template class=\"nsp-popup-tpl\" data-id=\"3\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<i class=\"nsp-pop-icon fa-solid fa-section\" aria-hidden=\"true\"><\/i>\t\t\t\t<span class=\"nsp-dlg-h-title\">RAG-driven Legal Chatbot<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>An AI-supported RAG-Chatbot developed for legal and administrative workflows. Designed to support caseworkers in navigating complex regulations - currently focused on German Social Welfare - the system provides fast, contextual access to relevant legal information and assists in decision-making for applications and case management. The modular architecture allows seamless expansion into additional legal domains and regulatory frameworks.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/nlp\">Natural Language Processing<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"7\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<span class=\"nsp-pop-icon et-pb-icon\" aria-hidden=\"true\">&#xe0f7;<\/span>\t\t\t\t<span class=\"nsp-dlg-h-title\">Generative AI Audit Framework<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>Development of a multidimensional Blackbox Chatbot Audit framework for evaluating chatbot user experience and business value in customer service environments. The audit methodology combines structured use-case testing with qualitative and quantitative evaluation dimensions, including answer quality, response speed, dialogue quality, and user interface assessment.<\/p>\n<p>The framework also incorporates hallucination testing and edge-case analysis to assess robustness and real-world usability. The project included extensive market and user-frustration research, methodology development, pilot implementation, and iterative testing and retesting phases. The resulting audit framework is used to evaluate chatbot performance, identify optimization potential, and assess user retention likelihood and overall business impact.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/governance-and-ethics\">Governance<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/nlp\">Natural Language Processing<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"12\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<span class=\"nsp-pop-icon et-pb-icon\" aria-hidden=\"true\">&#xe0f7;<\/span>\t\t\t\t<span class=\"nsp-dlg-h-title\">Intelligent Data Retrieval Agent<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>Development of a production-ready retrieval agent for querying large, fragmented, and undocumented enterprise data repositories using large language models and semantic search. The solution transformed scattered legacy records into structured, searchable knowledge, enabling users to retrieve relevant information within seconds instead of manually searching across hundreds of documents.<\/p>\n<p>The project combined retrieval-augmented generation (RAG), vector search, and modern LLM technologies to deliver reliable, context-aware information retrieval. Designed with a modular architecture, the system supports scalability, maintainability, and future extensions while ensuring robust retrieval quality across heterogeneous data sources.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--below\"><p>AI Engineer<\/p>\n<p>Python \u2022 LangChain \u2022 OpenAI \u2022 Qdrant \u2022 Semantic Search \u2022 RAG \u2022 Streamlit<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/nlp\">Natural Language Processing<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/control-and-automation\">Automation<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"2\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<span class=\"nsp-pop-icon et-pb-icon\" aria-hidden=\"true\">&#x77;<\/span>\t\t\t\t<span class=\"nsp-dlg-h-title\">Customer Interaction Analysis<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>For a project management SaaS solution where customers were matched with freelancers, a custom AI solution was established with the purpose to improve experiences for all parties. Key purposes were to create an early warning system to help customer service intervene in case of issues:<\/p>\n<ul>\n<li>Identification of unusual patterns (delays indicating inaction, intense exchanges);<\/li>\n<li>Flagging of language transgressions on both sides (use of inappropriate language, aggression);<\/li>\n<li>Matching of final ratings with evaluation of flow and dialogue quality to foster a more honest rating culture;<\/li>\n<li>Language style matching to improve future matching of freelancers to clients;<\/li>\n<\/ul>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--below\"><p>The solution was implemented using Python on a LAMP stack, with self-developed machine learning libraries.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/nlp\">Natural Language Processing<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><\/div><div class=\"nsp-overlay\" id=\"nsp-overlay\" role=\"dialog\" aria-modal=\"true\"><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"et_pb_section_7 et_pb_section et_section_regular et_flex_section ns-panel preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\" id=\"cv\"><div class=\"et_pb_row_11 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_18 et_pb_column et_pb_column_2_3 et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_12 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--group--divi-text--divi-font-body--h1yjkjr--7p5s44libg preset--module--divi-text--0431a145-bb8b-4440-b3c5-0a16c179fb90\"><div class=\"et_pb_text_inner\"><p>As formidable as the interplay of the human eye, brain and muscles is, it evolved to focus on what matters and filter out the rest. Machine vision has no such filter: it inspects every pixel with the same attention, frame after frame, around the clock. It never gets tired, and it works reliably in environments where humans are unsafe or uncomfortable.<\/p>\n<\/div><\/div><div class=\"et_pb_code_7 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\">\t<div\n\t\tid=\"ns-aix-3\"\n\t\tclass=\"ns-aix ns-aix-cv\"\n\t\tdata-topic=\"cv\"\n\t\tdata-flow=\"core\"\n\t\t\t\tdata-step=\"0\"\n\t\tdata-interval=\"9000\"\n\t\t\t\tdata-autoplay=\"1\"\n\t\trole=\"region\"\n\t\taria-label=\"How computer vision works\"\n\t\tstyle=\"--ns-aix-accent:#58a7f9;--ns-aix-secondary:#2b6cb0;--ns-aix-ink:#D6D6D6\"\n\t>\n\t\t<header class=\"ns-aix-head\">\n\t\t\t\t\t\t<h3 class=\"ns-aix-title\">How computer vision works<\/h3>\n\t\t\t<p class=\"ns-aix-intro\">Computer vision turns visual signals into numerical data \u2014 an image is a grid of numbers \u2014 then extracts task-relevant patterns and produces structured outputs such as labels, locations, masks, text, measurements or motion. It does not see as a person does; it estimates probabilities from visual evidence learned for a specific purpose.<\/p>\n\t\t\t\t\t\t\t\t<\/header>\n\n\t\t<div class=\"ns-aix-body\">\n\t\t\t<div class=\"ns-aix-copy\">\n\t\t\t\t<div class=\"ns-aix-progress\" aria-hidden=\"true\"><span><\/span><\/div>\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\tid=\"ns-aix-3-flow-panel-core\"\n\t\t\t\t\t\tclass=\"ns-aix-step-panel ns-on\"\n\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\t\t\t\t\t\t>\n\t\t\t\t\t<ol class=\"ns-aix-steps ns-on\" data-flow=\"core\" role=\"list\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step ns-on\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\t\tdata-key=\"capture\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"true\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-0\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">01<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Capture<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Light and other signals become input.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\t\tdata-key=\"encode\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-1\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">02<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Encode<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Pixels, channels and frames.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\t\tdata-key=\"prepare\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-2\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">03<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Prepare<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Correct noise, scale and viewpoint.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\t\tdata-key=\"learn\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-3\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">04<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Learn<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Edges become shapes and objects.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\t\tdata-key=\"recognize\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-4\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">05<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Recognize<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Classify, detect and find anomalies.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\t\tdata-key=\"map\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-5\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">06<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Map<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Masks, landmarks, depth and text.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\t\tdata-key=\"track\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-6\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">07<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Track<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Identity and motion persist between frames.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\t\t\tdata-key=\"act\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-7\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">08<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Act<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Measure, count, alert or control.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\t\tdata-step=\"8\"\n\t\t\t\t\t\t\t\t\tdata-key=\"validate\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-8\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">09<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Validate<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Performance, bias, drift and oversight.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<\/ol>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t<div class=\"ns-aix-stage\" aria-label=\"Animated vector-map process diagram\">\n\t\t\t\t<canvas class=\"ns-aix-canvas\" aria-hidden=\"true\"><\/canvas>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-on\" data-flow=\"core\" data-step=\"0\" data-key=\"capture\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>sensor<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"1\" data-key=\"encode\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>pixels<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"2\" data-key=\"prepare\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>prepare<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"3\" data-key=\"learn\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>features<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"4\" data-key=\"recognize\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>detect<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"5\" data-key=\"map\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>map<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"6\" data-key=\"track\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>track<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"7\" data-key=\"act\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>act<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"core\" data-step=\"8\" data-key=\"validate\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>check<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<\/div>\n\n\t\t<div class=\"ns-aix-controls\">\n\t\t\t<div class=\"ns-aix-dots\" role=\"navigation\" aria-label=\"Step navigation\">\n\t\t\t\t\t\t\t\t\t<span\n\t\t\t\t\t\tclass=\"ns-aix-dotset ns-on\"\n\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\trole=\"group\"\n\t\t\t\t\t\taria-label=\"Steps for How computer vision works\"\n\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot ns-on\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\taria-label=\"Step 1: Capture\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-0\"\n\t\t\t\t\t\t\t\taria-pressed=\"true\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\taria-label=\"Step 2: Encode\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-1\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\taria-label=\"Step 3: Prepare\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-2\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\taria-label=\"Step 4: Learn\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-3\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\taria-label=\"Step 5: Recognize\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-4\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\taria-label=\"Step 6: Map\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-5\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\taria-label=\"Step 7: Track\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-6\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\t\taria-label=\"Step 8: Act\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-7\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\t\tdata-step=\"8\"\n\t\t\t\t\t\t\t\taria-label=\"Step 9: Validate\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-3-panel-core-8\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\n\t\t<div class=\"ns-aix-below\" aria-live=\"polite\">\n\t\t\t<div class=\"ns-aix-explain\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-0\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel ns-on\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">01<\/span>Measure the scene<\/h4>\n\t\t\t\t\t\t\t<p>Computer vision begins with a sensor: a camera, scanner, microscope, satellite, X-ray device, thermal imager or depth sensor. Lens, viewpoint, exposure, resolution and frame rate determine which evidence enters the system. Detail that was never captured cannot be reconstructed reliably later.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-1\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">02<\/span>Encode into pixels<\/h4>\n\t\t\t\t\t\t\t<p>A digital image is a grid of pixels. Each pixel stores channel values such as red, green and blue, grayscale intensity, infrared response or depth. Video adds time as a sequence of frames. Resolution, bit depth and compression determine how much visual information is retained or discarded.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-2\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">03<\/span>Prepare the input<\/h4>\n\t\t\t\t\t\t\t<p>Images may be resized, cropped, denoised, sharpened, rectified for lens or perspective distortion and normalized for color or illumination. Training data is often augmented with realistic variation. Preparation should match operating conditions.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-3\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">04<\/span>Learn visual patterns<\/h4>\n\t\t\t\t\t\t\t<p>Traditional vision systems rely on engineered edges, corners and templates. Modern convolutional networks and vision transformers learn feature hierarchies from examples: simple contrast patterns combine into textures, shapes, parts and spatial relationships. This learning happens once, before deployment.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-4\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">05<\/span>Recognize objects and anomalies<\/h4>\n\t\t\t\t\t\t\t<p>Image classification assigns a label to an entire image. Object detection finds individual instances and their locations, usually with bounding boxes and confidence scores. Anomaly detection instead learns what normal visual data looks like and flags deviations. A high confidence score is not the same as a high chance of being correct.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-5\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">06<\/span>Map regions, depth and text<\/h4>\n\t\t\t\t\t\t\t<p>Segmentation assigns a category or object identity to pixels. Keypoint and pose models locate landmarks; depth models estimate distance and three-dimensional structure; OCR and document-layout systems recover characters, fields and reading order.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-6\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">07<\/span>Track objects over time<\/h4>\n\t\t\t\t\t\t\t<p>Video systems associate detections between frames so an object keeps a stable identity. From trajectories they can estimate direction, speed, flow, dwell time and actions, or count objects crossing a boundary.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-7\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">08<\/span>Turn perception into action<\/h4>\n\t\t\t\t\t\t\t<p>Vision output becomes useful when another process consumes it: count or sort an item, populate a data table, alert an operator, trigger maintenance or guide a robot. Thresholds convert probability scores into actions. When the subject is people, privacy and consent constrain what the system may capture, store or act on.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-3-panel-core-8\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"core\"\n\t\t\t\t\t\t\tdata-step=\"8\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">09<\/span>Test in the real world<\/h4>\n\t\t\t\t\t\t\t<p>A vision system must be tested on separate data across lighting, weather, camera positions, demographic groups, rare cases and deliberate interference. Precision, recall, false positives, false negatives, latency and calibration all matter. Monitoring and human corrections detect drift and improve the system safely.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<div class=\"ns-aix-aspects\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset ns-on\" data-flow=\"core\" data-step=\"0\" aria-hidden=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Background<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Early computer vision began in the 1960s and 1970s with programs recognizing simple shapes \u2014 and with optical character recognition (OCR) for typed documents as one of the first practical uses.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Camera choice, placement and lighting often decide project success before any model is trained.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Vision systems extract meaning from pixels represented as numerical values, detecting patterns across those numbers to identify objects, shapes, textures and motion.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Compression and low resolution silently discard evidence the model may later need \u2014 what is lost here is lost for good.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"2\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>During training the model is shown many labeled examples, makes predictions, compares them with the correct labels and adjusts internal parameters to reduce errors.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Excessive cleaning can remove the very defect or signal the system must find.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"3\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Background<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Progress accelerated in the 2000s with better hardware and large datasets; a major breakthrough came in 2012, when deep learning sharply improved recognition accuracy.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Deep neural networks learn visual patterns directly from large collections of labeled images instead of relying on hand-written rules.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"4\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>What CV can do<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Finding irregularities on assembly lines, spotting trespassers, categorizing items and recognizing faces \u2014 often detecting differences the human eye misses.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Accuracy can vary across demographic groups when training data is biased \u2014 a known and serious risk in facial recognition.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"5\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>What CV can do<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>OCR combined with AI can understand almost any business document, fill data tables accurately and support decisions about how to handle the input.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Segmentation, landmarks and depth support measurement, inspection, navigation and document processing tasks.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"6\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>What CV can do<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Counting people in crowds, following items at night or at high speed, measuring flow \u2014 often replacing expensive sensing technology or hours of human labor.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Occlusion, motion blur, camera movement and objects re-entering the scene can break a track and corrupt counts.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"7\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The system does not &quot;recognize&quot; items in a human sense \u2014 it calculates probabilities from learned visual patterns, which can produce dangerous errors if acted on blindly.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The cost of a missed event versus a false alarm should decide when automation acts and when a person reviews.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"core\" data-step=\"8\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Vision systems struggle with unknown objects, unfamiliar conditions and overlap, and drift appears as the world changes around a frozen model.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>In regulated or safety-relevant uses, documented test results across conditions and demographic groups turn a working demo into a system that can be trusted and audited.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\t<\/div><\/div><\/div><div class=\"et_pb_column_19 et_pb_column et_pb_column_1_3 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_13 et_pb_text et_pb_bg_layout_light et_pb_module et_flex_module preset--group--divi-text--divi-box-shadow--default preset--group--divi-text--divi-font-body--h1yjkjr--7p5s44libg preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2>The Challenges<\/h2>\n<p style=\"text-align: justify;\">Despite their capabilities often surpassing human vision, Computer Vision systems have limitations. They struggle with unknown information, unfamiliar conditions, or overlapping objects. Equally, while they can even detect the smallest changes, learning what is relevant and what isn\u2019t can be hard.<\/p>\n<p style=\"text-align: justify;\">Another concern is bias, for example, when it comes to facial recognition. Often, due to biased training data, their accuracy varies across various ethnic groups. And ultimately, they do not \u201crecognize\u201d items, but only statistical patterns. This can create dangerous errors, for example in facial recognition. As with all tools, careful oversight and governance are required.<\/p>\n<p style=\"text-align: justify;\">Additionally, the challenges emerging from image generation create entirely new ethical and regulatory problems.<\/p>\n<\/div><\/div><\/div><\/div><div class=\"et_pb_row_12 et_pb_row et_flex_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_20 et_pb_column et-last-child et_flex_column et_pb_css_mix_blend_mode_passthrough et_flex_column_24_24 et_flex_column_24_24_tablet et_flex_column_24_24_phone et_flex_column_24_24_tabletWide preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_code_8 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\"><div class=\"nsp-frame nsp-frame--transparent\"><div class=\"nsp-frame-head\"><h2>Featured Projects<\/h2><\/div><div class=\"nsp-carousel-wrap\" data-base-width=\"240\" data-loop=\"1\"><button type=\"button\" class=\"nsp-nav nsp-prev\" aria-label=\"Previous\"><span class=\"nsp-nav-icon et-pb-icon\" aria-hidden=\"true\">&#x34;<\/span><\/button><div class=\"nsp-carousel\">\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"10\" aria-label=\"Facility Management Process Digitalization\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><span class=\"nsp-card-icon et-pb-icon\" aria-hidden=\"true\">&#xe035;<\/span><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Facility Management Process Digitalization<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Transformation and standardization of international facility management processes through the introduction of scalable structures.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"11\" aria-label=\"ERP\/CRM Product Development\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><span class=\"nsp-card-icon et-pb-icon\" aria-hidden=\"true\">&#xe00d;<\/span><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">ERP\/CRM Product Development<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Development and implementation of a modular ERP\/CRM system to digitalize central business processes.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"13\" aria-label=\"Visual Search Recommendations\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-socks\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Visual Search Recommendations<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">eCommmerce plugin that enables searching for visually similar products, helping customers to find and compare multiple related items.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"14\" aria-label=\"Visual Assistance for Seniors\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-glasses\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Visual Assistance for Seniors<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Visual Assistance App: enabling visual assistance for seniors by helping position determination using Computer Vision and Deep Learning.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"1\" aria-label=\"Electrical Switch Monitor\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-toggle-on\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Electrical Switch Monitor<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Public transportation: using AI-driven vision to monitor old-fashioned electrical relays and also to evaluate potential failures for predictive maintenance.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t\t\t<article class=\"nsp-card\" tabindex=\"0\" role=\"button\" data-id=\"5\" aria-label=\"Hydropower Plant Operations\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-card-iconwrap\"><i class=\"nsp-card-icon fa-solid fa-water\" aria-hidden=\"true\"><\/i><\/div>\n\t\t\t\t\t\t<h3 class=\"nsp-card-title\">Hydropower Plant Operations<\/h3>\n\t\t\t\t\t\t\t\t\t\t<p class=\"nsp-card-excerpt\">Create a control and monitoring solution for all plant operations, including predictive maintenance logic and intrusion mon\u00aditoring.<\/p>\n\t\t\t\t\t\t\t\t\t\t<span class=\"nsp-card-more\">read more\u2026<\/span>\n\t\t\t\t\t<\/article>\n\t\t<\/div><button type=\"button\" class=\"nsp-nav nsp-next\" aria-label=\"Next\"><span class=\"nsp-nav-icon et-pb-icon\" aria-hidden=\"true\">&#x35;<\/span><\/button><\/div><script>(function(){\n\t\t\tif (!window.nspComputeLayout) {\n\t\t\t\twindow.NSP_BASE_CARD_WIDTH = 240;\n\t\t\t\twindow.nspIsFluidWidth = function(){ return window.innerWidth > 640; };\n\t\t\t\twindow.nspComputeLayout = function(available, gap, totalCards, baseWidth){\n\t\t\t\t\tvar BASE = baseWidth || window.NSP_BASE_CARD_WIDTH;\n\t\t\t\t\tavailable = Math.max(0, available || 0);\n\t\t\t\t\tgap = Math.max(0, gap || 0);\n\t\t\t\t\ttotalCards = Math.max(0, totalCards || 0);\n\t\t\t\t\tif (!totalCards){\n\t\t\t\t\t\treturn { count: 0, width: BASE, center: true };\n\t\t\t\t\t}\n\t\t\t\t\tvar per = BASE + gap;\n\t\t\t\t\tvar baseCount = Math.max(1, Math.floor((available + gap) \/ per));\n\t\t\t\t\tvar used = baseCount * BASE + (baseCount - 1) * gap;\n\t\t\t\t\tvar remainder = available - used;\n\t\t\t\t\t\/\/ Only create an extra visible slot when there is actually another card to fill it.\n\t\t\t\t\t\/\/ If exactly the base-width row count is present, distribute those cards evenly\n\t\t\t\t\t\/\/ across the row instead of leaving them beside a phantom extra slot.\n\t\t\t\t\tvar fullCount = (totalCards > baseCount && remainder >= (2 \/ 3) * BASE) ? baseCount + 1 : baseCount;\n\t\t\t\t\tfullCount = Math.max(1, fullCount);\n\t\t\t\t\tvar fullWidth = (available - (fullCount - 1) * gap) \/ fullCount;\n\t\t\t\t\tif (!isFinite(fullWidth) || fullWidth <= 0){ fullWidth = BASE; }\n\n\t\t\t\t\t\/\/ One or two cards should never grow just because the row is wide.\n\t\t\t\t\t\/\/ They keep the base width, sit at the left, and leave the rest of the row empty.\n\t\t\t\t\tif (totalCards < 3){\n\t\t\t\t\t\tvar smallMax = (available - (totalCards - 1) * gap) \/ totalCards;\n\t\t\t\t\t\tvar smallWidth = (isFinite(smallMax) && smallMax > 0) ? Math.min(BASE, smallMax) : BASE;\n\t\t\t\t\t\treturn { count: totalCards, width: smallWidth, center: false };\n\t\t\t\t\t}\n\n\t\t\t\t\t\/\/ If there are fewer cards than a full row would hold, keep the full-row\n\t\t\t\t\t\/\/ card width rather than stretching those few across the whole row, and\n\t\t\t\t\t\/\/ leave the remaining slots empty. Left-aligned, as the master grid is.\n\t\t\t\t\tif (totalCards < fullCount){\n\t\t\t\t\t\treturn { count: totalCards, width: fullWidth, center: false };\n\t\t\t\t\t}\n\n\t\t\t\t\tvar count = Math.min(fullCount, totalCards);\n\t\t\t\t\treturn { count: count, width: fullWidth, center: false };\n\t\t\t\t};\n\t\t\t\t\/\/ Centre the chevrons on the media block of a card rather than on\n\t\t\t\t\/\/ the whole card: on people cards that is the square photo\n\t\t\t\t\/\/ (whose height tracks the fluid card width, so it cannot be\n\t\t\t\t\/\/ expressed in CSS), on project cards the icon block. Published\n\t\t\t\t\/\/ as a custom property the stylesheet reads.\n\t\t\t\twindow.nspTrackPadX = function(track){\n\t\t\t\t\tvar cs = window.getComputedStyle(track);\n\t\t\t\t\treturn (parseFloat(cs.paddingLeft) || 0) + (parseFloat(cs.paddingRight) || 0);\n\t\t\t\t};\n\t\t\t\twindow.nspSyncNav = function(wrap){\n\t\t\t\t\t\/\/ People cards anchor on the photo, which is the visual mass of\n\t\t\t\t\t\/\/ the card. Project cards have no photo -- only a small icon\n\t\t\t\t\t\/\/ at the top -- so they anchor on the card panel itself and\n\t\t\t\t\t\/\/ the arrows land on its vertical centre. Measuring against\n\t\t\t\t\t\/\/ the wrap in viewport coordinates absorbs the track padding\n\t\t\t\t\t\/\/ and card margins without restating any of them here.\n\t\t\t\t\tvar media = wrap.querySelector(\".nsp-card-photo\")\n\t\t\t\t\t\t|| wrap.querySelector(\".nsp-card\");\n\t\t\t\t\tif (!media){ return; }\n\t\t\t\t\tvar r = media.getBoundingClientRect();\n\t\t\t\t\tif (!r.height){ return; }\n\t\t\t\t\tvar w = wrap.getBoundingClientRect();\n\t\t\t\t\twrap.style.setProperty(\"--nsp-nav-top\", ((r.top - w.top) + r.height \/ 2) + \"px\");\n\t\t\t\t};\n\t\t\t}\n\t\t\tvar wrap = document.currentScript.previousElementSibling;\n\t\t\tif(!wrap){ return; }\n\t\t\t\/\/ The frame carries the visible panel, so it has to be revealed\n\t\t\t\/\/ together with the wrap -- otherwise an empty framed box paints\n\t\t\t\/\/ before the cards have been sized into it.\n\t\t\tfunction reveal(w){\n\t\t\t\tw.style.visibility = \"visible\";\n\t\t\t\tvar f = w.parentNode;\n\t\t\t\tif (f && f.classList && f.classList.contains(\"nsp-frame\")){ f.style.visibility = \"visible\"; }\n\t\t\t}\n\t\t\tvar baseWidth = parseInt(wrap.getAttribute(\"data-base-width\"), 10) || window.NSP_BASE_CARD_WIDTH;\n\t\t\tvar track = wrap.querySelector(\".nsp-carousel\");\n\t\t\tvar cards = track ? track.querySelectorAll(\".nsp-card\") : [];\n\t\t\tif(!track || !cards.length){ if (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap); return; }\n\t\t\t\/\/ Read from the stylesheet rather than restating it: a hard-coded copy\n\t\t\t\/\/ here is exactly how the gutter drifted from the CSS before.\n\t\t\tvar gap = parseFloat(window.getComputedStyle(track).columnGap) || 24;\n\t\t\tif (!window.nspIsFluidWidth()) {\n\t\t\t\t\/\/ Mobile: unchanged -- fixed per-card width (CSS 82vw), just avoid a partial\n\t\t\t\t\/\/ card peeking out at the wrap edge.\n\t\t\t\tvar cardW = cards[0].getBoundingClientRect().width;\n\t\t\t\tif(!cardW){ if (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap); return; }\n\t\t\t\t\/\/ clientWidth is the PADDING box, so the track padding has to come\n\t\t\t\t\/\/ off before this is the room the cards actually get.\n\t\t\t\tvar available0 = track.clientWidth - window.nspTrackPadX(track);\n\t\t\t\tvar fitCount = Math.max(1, Math.floor((available0 + gap) \/ (cardW + gap)));\n\t\t\t\tvar count0 = Math.min(fitCount, cards.length);\n\t\t\t\tvar needed0 = count0 * cardW + (count0 - 1) * gap;\n\t\t\t\tvar ws0 = window.getComputedStyle(wrap);\n\t\t\t\tvar padX0 = (parseFloat(ws0.paddingLeft) || 0) + (parseFloat(ws0.paddingRight) || 0);\n\t\t\t\tvar target0 = needed0 + window.nspTrackPadX(track) + (ws0.boxSizing === \"border-box\" ? padX0 : 0);\n\t\t\t\twrap.style.maxWidth = Math.ceil(target0) + \"px\";\n\t\t\t\tif (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\t\/\/ Desktop\/tablet: fluid card width, filling the row exactly either way.\n\t\t\t\/\/ clientWidth is the PADDING box -- take the track padding off it.\n\t\t\tvar available = track.clientWidth - window.nspTrackPadX(track);\n\t\t\tvar layout = window.nspComputeLayout(available, gap, cards.length, baseWidth);\n\t\t\tfor (var i = 0; i < cards.length; i++){ cards[i].style.flex = \"0 0 \" + layout.width + \"px\"; }\n\t\t\tif (layout.center) {\n\t\t\t\tvar totalW = layout.count * layout.width + (layout.count - 1) * gap;\n\t\t\t\twrap.style.maxWidth = Math.ceil(totalW + window.nspTrackPadX(track)) + \"px\";\n\t\t\t\twrap.style.marginLeft = \"auto\";\n\t\t\t\twrap.style.marginRight = \"auto\";\n\t\t\t}\n\t\t\tif (window.nspSyncNav){ window.nspSyncNav(wrap); }\n\t\t\treveal(wrap);\n\t\t})();<\/script><\/div><noscript><style>.nsp-carousel-wrap,.nsp-frame{visibility:visible !important;}<\/style><\/noscript><div class=\"nsp-popups\" hidden><template class=\"nsp-popup-tpl\" data-id=\"10\">\t\t<div class=\"nsp-dialog has-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t\t\t\t\t<span class=\"nsp-dlg-h-title\">Facility Management Process Digitalization<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>Supported the transformation and standardization of international facility management processes by designing scalable end-to-end process and service structures.<\/p>\n<p>The project included gathering and harmonizing requirements across multiple country organizations, translating business needs into digital process and system solutions, and coordinating international rollouts including SIT, UAT, training, and change management.<\/p>\n<p>Consistent process modeling and documentation using BPMN 2.0 and SAP Signavio helped establish sustainable governance, transparency, and operational efficiency.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-aside\">\n\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"768\" src=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/Image1-1024x768.png\" class=\"nsp-pop-img\" alt=\"\" srcset=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/Image1-980x735.png 980w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/Image1-480x360.png 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) 1024px, 100vw\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--aside\"><p>SAP Signavio, BPMN 2.0, ERP Systems, Digital Workflow Platforms, Interface Integration, SIT\/UAT, Requirements Management<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/control-and-automation\">Automation<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/automation-and-control\">Infrastructure Management<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tHuman-Technology Interaction\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"11\">\t\t<div class=\"nsp-dialog has-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t\t\t\t\t<span class=\"nsp-dlg-h-title\">ERP\/CRM Product Development<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>Led the development and implementation of a modular ERP\/CRM system to digitalize central business processes.<\/p>\n<p>The project included end-to-end product ownership, requirements analysis, prioritization, and scaling of the system, including mobile solutions and extensions.<\/p>\n<p>Responsibilities also covered the management of cross-functional development teams, the establishment of testing, quality, and operations processes, and the introduction of ITIL-based change and incident structures. Governance, KPI, PMO, and documentation standards were developed to support sustainable product and project management, while product strategy and stakeholder alignment were managed at leadership level.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-aside\">\n\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"768\" src=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/Image2-1024x768.png\" class=\"nsp-pop-img\" alt=\"\" srcset=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/Image2-980x735.png 980w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/Image2-480x360.png 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) 1024px, 100vw\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--aside\"><p>ERP\/CRM Systems, Mobile Solutions, Agile Product Development, Requirements Management, UAT, ITIL, Change &amp; Incident Management, KPI\/PMO Structures, Stakeholder Management<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/control-and-automation\">Automation<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/automation-and-control\">Infrastructure Management<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/data-and-knowledge-management\/\">Data Warehouse<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tHuman-Technology Interaction\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"13\">\t\t<div class=\"nsp-dialog has-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t\t\t\t\t<span class=\"nsp-dlg-h-title\">Visual Search Recommendations<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>For VisualSearch, the focus was on developing and deploying e-commerce plugins to enhance recommendation functionalities in web shops. The plugins were successfully launched in the store and happily adopted by customers, demonstrating their practical use in improving e-commerce experiences. Key elements included:<br \/>\n- computation of visual embeddings from appearances of e-commerce products<br \/>\n- building and maintaining a search index using these embeddings<br \/>\n- providing a cloud-based API for Shopware and Prestashop plugins<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-aside\">\n\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"369\" height=\"455\" src=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/visualsearch.png\" class=\"nsp-pop-img\" alt=\"\" srcset=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/visualsearch.png 369w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/visualsearch-243x300.png 243w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/visualsearch-10x12.png 10w\" sizes=\"(max-width: 369px) 100vw, 369px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--aside\"><p>Peter acted as the project manager and key data scientist on this project, leveraging AWS services, Python, and deep learning frameworks like Keras. The project integrated cloud-based solutions using CloudFormation, Lambda, and Gateway for scalable and efficient deployment, using SQL and DynamoDB for data management.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"14\">\t\t<div class=\"nsp-dialog has-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t\t\t\t\t<span class=\"nsp-dlg-h-title\">Visual Assistance for Seniors<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>For the Austrian Institute of Technology (as a part of the national research project LARAH), a prototype of an assistance system for visual indoor localization for disabled and elderly persons was developed. By leveraging Computer Vision and Deep Learning, innovative algorithms for position determination were implemented. The project resulted in a functional prototype, including two Android applications for real-time localization. Key elements included:<br \/>\n- visual recognition of persons using Deep-Learning models<br \/>\n- visual reconstruction and localization of indoor environments using Structure-from-Motion and Machine Learning algorithms<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-aside\">\n\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"366\" height=\"451\" src=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/larah.png\" class=\"nsp-pop-img\" alt=\"\" srcset=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/larah.png 366w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/larah-243x300.png 243w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/larah-10x12.png 10w\" sizes=\"(max-width: 366px) 100vw, 366px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--aside\"><p>Peter acted as the project manager and key data scientist on this project, developed under Python using Deep Learning models like TensorFlow, custom-developed Structure-from-Motion software and custom camera calibration software. Additionally, as a part of the project, two Android apps were modified and integrated together onto the Robot Operating System on the mobile platform.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"1\">\t\t<div class=\"nsp-dialog has-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t\t\t\t\t<span class=\"nsp-dlg-h-title\">Electrical Switch Monitor<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><p>For a public transportation network, the objective was to optimize the monitoring of their legacy electrical switchboards. These decade-old items that are often located in very remote areas are prone to failures and tracking of errors was not possible. The objective was to enable real-time tracking and the recognition of upcoming failures from changed switching behavior. The key elements were:<\/p>\n<ul>\n<li>Development of specific hardware configuration with custom housings (3D printed) to mount instead of regular switchboard covers;<\/li>\n<li>Camera control and initial image generation on Raspberry Pi integrated in housing;<\/li>\n<li>Initial scan of switch layout and labels;<\/li>\n<li>Identification of switching operations and registration of new positions;<\/li>\n<li>Identification of irregular switching patterns (delays, other irregularities) to indicate upcoming failures for predictive maintenance;<\/li>\n<li>Update of central database and cloud solution with last state and observed switching patterns;<\/li>\n<\/ul>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-aside\">\n\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"959\" src=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/6by6cropped-1024x959.jpg\" class=\"nsp-pop-img\" alt=\"\" srcset=\"https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/6by6cropped-980x918.jpg 980w, https:\/\/www.9senses.ai\/wp-content\/uploads\/2026\/07\/6by6cropped-480x450.jpg 480w\" sizes=\"(min-width: 0px) and (max-width: 480px) 480px, (min-width: 481px) and (max-width: 980px) 980px, (min-width: 981px) 1024px, 100vw\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--aside\"><p>The solution was implemented using Python on Raspberry Pi devices, backbone and cloud processing were done using a LAMP stack, with PyTorch, TensorFlow and OpenCV.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><template class=\"nsp-popup-tpl\" data-id=\"5\">\t\t<div class=\"nsp-dialog no-image\">\n\t\t\t<button type=\"button\" class=\"nsp-close\" aria-label=\"Close\">&times;<\/button>\n\t\t\t<div class=\"nsp-dlg-h\">\n\t\t\t\t<i class=\"nsp-pop-icon fa-solid fa-water\" aria-hidden=\"true\"><\/i>\t\t\t\t<span class=\"nsp-dlg-h-title\">Hydropower Plant Operations<\/span>\n\t\t\t<\/div>\n\t\t\t<div class=\"nsp-pop-cols\">\n\t\t\t\t<div class=\"nsp-pop-main\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-desc\"><div class=\"et_pb_column_11 et_pb_column et_flex_column et_pb_css_mix_blend_mode_passthrough et_flex_column_6_24 et_flex_column_12_24_tablet et_flex_column_24_24_phone et_flex_column_12_24_tabletWide preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\">\n<div class=\"et_pb_blurb_2 et_pb_blurb et_pb_bg_layout_light et_pb_blurb_position_top et_pb_module et_flex_module hovergroup preset--group--divi-blurb--divi-box-shadow--default preset--group--divi-blurb--divi-font-body--h19rs5u--7p5s44libg preset--group--divi-blurb--divi-sizing--hsj9uxo--default\">\n<div class=\"et_pb_blurb_content et_flex_module\">\n<div class=\"et_pb_blurb_container\">\n<div class=\"et_pb_blurb_description\">\n<p>Create an integrated monitoring and surveillance solution for small-scale hydropower plants in remote locations. The solution included a full range of required settings:<\/p>\n<ul>\n<li>real-time monitoring and logging of operations<\/li>\n<li>failure detection and automated<\/li>\n<li>predictive maintenance logic to identify early failure<\/li>\n<li>camera-based intrusion and irregularity detection<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"nsp-pop-role nsp-pop-role--below\"><p>The solution was a hybrid solution using Siemens PLCs, combined with small edge computing elements (Raspberry Pi and Arduino). All primary logic (particularly shutdown and load adjustment) was local, but key decisions and aggregations were executed online based on regular data transmission to a cloud-based management and operations suite.<\/p>\n<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t<ul class=\"nsp-chiplist nsp-pop-caps\">\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/machine-learning\">Machine Learning<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/vision\">Computer Vision<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nsp-chip\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/automation-and-control\">Infrastructure Management<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t<\/div>\n\t\t<\/template><\/div><div class=\"nsp-overlay\" id=\"nsp-overlay\" role=\"dialog\" aria-modal=\"true\"><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"et_pb_section_8 et_pb_section et_section_regular et_flex_section ns-panel preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\" id=\"robotics\"><div class=\"et_pb_row_13 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_21 et_pb_column et_pb_column_2_3 et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_14 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--group--divi-text--divi-font-body--h1yjkjr--7p5s44libg preset--module--divi-text--0431a145-bb8b-4440-b3c5-0a16c179fb90\"><div class=\"et_pb_text_inner\"><p><span>Of all AI fields, robotics is the one where software has consequences in the physical world: sensor data has to be turned into safe, precise motion in real time, and there is no undo button. That step from calculating to acting is what makes the field so demanding, and why progress here is slower than in purely digital AI.<\/span><\/p>\n<\/div><\/div><div class=\"et_pb_code_9 et_pb_code et_pb_module preset--group--divi-code--divi-box-shadow--default\"><div class=\"et_pb_code_inner\">\t<div\n\t\tid=\"ns-aix-4\"\n\t\tclass=\"ns-aix ns-aix-rob ns-aix-rob3d\"\n\t\tdata-topic=\"rob\"\n\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\tdata-journey=\"industrial\"\n\t\tdata-active-key=\"task\"\n\t\t\t\tdata-step=\"0\"\n\t\tdata-interval=\"9000\"\n\t\t\t\tdata-autoplay=\"1\"\n\t\trole=\"region\"\n\t\taria-label=\"How robotics works\"\n\t\tstyle=\"--ns-aix-accent:#58a7f9;--ns-aix-secondary:#2b6cb0;--ns-aix-ink:#D6D6D6\"\n\t>\n\t\t<header class=\"ns-aix-head\">\n\t\t\t\t\t\t<h3 class=\"ns-aix-title\">How robotics works<\/h3>\n\t\t\t<p class=\"ns-aix-intro\">Robotics is where AI and automation become physical. Sensors measure the world, software estimates what is happening, controllers choose safe movement, and actuators move real objects. The loop only works when safety, verification and governance are designed into the system.<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-flowgroup ns-aix-flowgroup-shaped\">\n\t\t\t\t\t\t\t\t\t\t\t<svg class=\"ns-aix-flowshape\" aria-hidden=\"true\" focusable=\"false\" preserveAspectRatio=\"none\">\n\t\t\t\t\t\t\t<path class=\"ns-aix-flowshape-fill\" d=\"\" \/>\n\t\t\t\t\t\t\t<path class=\"ns-aix-flowshape-glow\" d=\"\" \/>\n\t\t\t\t\t\t<\/svg>\n\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-tabs\" role=\"tablist\" aria-label=\"How robotics works\">\n\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\tid=\"ns-aix-4-tab-industrial\"\n\t\t\t\t\t\t\tclass=\"ns-aix-tab ns-on\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"true\"\n\t\t\t\t\t\t\taria-label=\"Show the industrial robot workcell journey\"\n\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\taria-controls=\"ns-aix-4-flow-panel-industrial\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\tIndustrial robot\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\tid=\"ns-aix-4-tab-autonomous\"\n\t\t\t\t\t\t\tclass=\"ns-aix-tab\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\trole=\"tab\"\n\t\t\t\t\t\t\taria-selected=\"false\"\n\t\t\t\t\t\t\taria-label=\"Show the autonomous robot journey\"\n\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\taria-controls=\"ns-aix-4-flow-panel-autonomous\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\tAutonomous robot\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-flow-intros\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<p\n\t\t\t\t\t\t\t\tid=\"ns-aix-4-flow-intro-industrial\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-flow-intro ns-on\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\tA factory robot can be highly capable because the workcell is deliberately constrained. Fixtures, coordinates, tools, speed limits and safety zones reduce uncertainty before the controller starts to move.\t\t\t\t\t\t\t<\/p>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<p\n\t\t\t\t\t\t\t\tid=\"ns-aix-4-flow-intro-autonomous\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-flow-intro\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\tAutonomy becomes harder when the boundaries disappear. A vehicle, rover or walking robot must estimate its own position, interpret a changing scene and choose safe action under uncertainty.\t\t\t\t\t\t\t<\/p>\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/header>\n\n\t\t<div class=\"ns-aix-body\">\n\t\t\t<div class=\"ns-aix-copy\">\n\t\t\t\t<div class=\"ns-aix-progress\" aria-hidden=\"true\"><span><\/span><\/div>\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\tid=\"ns-aix-4-flow-panel-industrial\"\n\t\t\t\t\t\tclass=\"ns-aix-step-panel ns-on\"\n\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\trole=\"tabpanel\" aria-labelledby=\"ns-aix-4-tab-industrial\"\t\t\t\t\t>\n\t\t\t\t\t<ol class=\"ns-aix-steps ns-on\" data-flow=\"industrial\" role=\"list\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step ns-on\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\t\tdata-key=\"task\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"true\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-0\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">01<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Task<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">A bounded task makes automation possible.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\t\tdata-key=\"sense\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-1\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">02<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Sense<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Cameras, encoders and force signals enter the loop.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\t\tdata-key=\"locate\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-2\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">03<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Locate<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Position only matters in a shared frame.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\t\tdata-key=\"plan\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-3\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">04<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Plan<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">A route is selected before movement begins.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\t\tdata-key=\"control\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-4\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">05<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Control<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Actuators turn commands into motion.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\t\tdata-key=\"verify\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-5\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">06<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Verify<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Success is measured, not assumed.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\t\tdata-key=\"protect\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-6\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">07<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Protect<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Physical action needs explicit boundaries.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\t\t\tdata-key=\"improve\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-7\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">08<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Improve<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Logs support maintenance and optimization.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<\/ol>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<div\n\t\t\t\t\t\tid=\"ns-aix-4-flow-panel-autonomous\"\n\t\t\t\t\t\tclass=\"ns-aix-step-panel\"\n\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\trole=\"tabpanel\" aria-labelledby=\"ns-aix-4-tab-autonomous\"\t\t\t\t\t>\n\t\t\t\t\t<ol class=\"ns-aix-steps\" data-flow=\"autonomous\" role=\"list\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\t\tdata-key=\"mission\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-0\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">01<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Mission<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">A mission includes rules, not only a target.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\t\tdata-key=\"perceive\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-1\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">02<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Perceive<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Multiple sensors reduce blind spots.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\t\tdata-key=\"localize\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-2\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">03<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Localize<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">The map is only useful with a live pose estimate.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\t\tdata-key=\"model\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-3\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">04<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Model<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Objects, space and risk are estimated.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\t\tdata-key=\"plan\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-4\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">05<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Plan<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Candidate paths compete under constraints.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\t\tdata-key=\"act\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-5\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">06<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Act<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Motion control adapts continuously.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\t\tdata-key=\"respond\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-6\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">07<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Respond<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Fallback is part of autonomy.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<li>\n\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\t\tclass=\"ns-aix-step\"\n\t\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\t\t\tdata-key=\"govern\"\n\t\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-7\"\n\t\t\t\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-num\">08<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-text\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-label\">Govern<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"ns-aix-step-micro\">Logs, limits and human override matter.<\/span>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<\/ol>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\n\t\t\t<div class=\"ns-aix-stage\" aria-label=\"Animated vector-map process diagram\">\n\t\t\t\t<canvas class=\"ns-aix-canvas\" aria-hidden=\"true\"><\/canvas>\n\t\t\t\t\t\t\t\t\t<canvas class=\"ns-aix-canvas3d\" aria-hidden=\"true\"><\/canvas>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-on\" data-flow=\"industrial\" data-step=\"0\" data-key=\"task\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>task<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"industrial\" data-step=\"1\" data-key=\"sense\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>sense<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"industrial\" data-step=\"2\" data-key=\"locate\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>frame<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"industrial\" data-step=\"3\" data-key=\"plan\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>plan<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"industrial\" data-step=\"4\" data-key=\"control\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>act<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"industrial\" data-step=\"5\" data-key=\"verify\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>verify<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"industrial\" data-step=\"6\" data-key=\"protect\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>safety<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node\" data-flow=\"industrial\" data-step=\"7\" data-key=\"improve\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>logs<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"autonomous\" data-step=\"0\" data-key=\"mission\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>mission<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"autonomous\" data-step=\"1\" data-key=\"perceive\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>sense<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"autonomous\" data-step=\"2\" data-key=\"localize\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>pose<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"autonomous\" data-step=\"3\" data-key=\"model\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>scene<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"autonomous\" data-step=\"4\" data-key=\"plan\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>plan<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"autonomous\" data-step=\"5\" data-key=\"act\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>act<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"autonomous\" data-step=\"6\" data-key=\"respond\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>fallback<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t<button type=\"button\" class=\"ns-aix-node ns-hidden\" data-flow=\"autonomous\" data-step=\"7\" data-key=\"govern\" tabindex=\"-1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t<span>govern<\/span>\n\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<\/div>\n\n\t\t<div class=\"ns-aix-controls\">\n\t\t\t<div class=\"ns-aix-dots\" role=\"navigation\" aria-label=\"Step navigation\">\n\t\t\t\t\t\t\t\t\t<span\n\t\t\t\t\t\tclass=\"ns-aix-dotset ns-on\"\n\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\trole=\"group\"\n\t\t\t\t\t\taria-label=\"Steps for Industrial robot\"\n\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot ns-on\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\taria-label=\"Step 1: Task\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-0\"\n\t\t\t\t\t\t\t\taria-pressed=\"true\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\taria-label=\"Step 2: Sense\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-1\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\taria-label=\"Step 3: Locate\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-2\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\taria-label=\"Step 4: Plan\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-3\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\taria-label=\"Step 5: Control\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-4\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\taria-label=\"Step 6: Verify\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-5\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\taria-label=\"Step 7: Protect\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-6\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\t\taria-label=\"Step 8: Improve\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-industrial-7\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span\n\t\t\t\t\t\tclass=\"ns-aix-dotset\"\n\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\trole=\"group\"\n\t\t\t\t\t\taria-label=\"Steps for Autonomous robot\"\n\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\t\taria-label=\"Step 1: Mission\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-0\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\t\taria-label=\"Step 2: Perceive\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-1\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\t\taria-label=\"Step 3: Localize\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-2\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\t\taria-label=\"Step 4: Model\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-3\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\t\taria-label=\"Step 5: Plan\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-4\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\t\taria-label=\"Step 6: Act\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-5\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\t\taria-label=\"Step 7: Respond\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-6\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<button\n\t\t\t\t\t\t\t\ttype=\"button\"\n\t\t\t\t\t\t\t\tclass=\"ns-aix-dot\"\n\t\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\t\taria-label=\"Step 8: Govern\"\n\t\t\t\t\t\t\t\taria-controls=\"ns-aix-4-panel-autonomous-7\"\n\t\t\t\t\t\t\t\taria-pressed=\"false\"\n\t\t\t\t\t\t\t\ttabindex=\"-1\"\n\t\t\t\t\t\t\t><\/button>\n\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\n\t\t<div class=\"ns-aix-below\" aria-live=\"polite\">\n\t\t\t<div class=\"ns-aix-explain\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-industrial-0\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel ns-on\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\taria-hidden=\"false\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">01<\/span>Define the physical job<\/h4>\n\t\t\t\t\t\t\t<p>Industrial robotics begins with a defined task: pick, place, weld, inspect, screw, pack or sort. The environment is engineered around the task, so the robot does not have to understand the whole world. Workpiece, fixture, tool and allowed path are specified before automation begins.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-industrial-1\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">02<\/span>Measure the workcell<\/h4>\n\t\t\t\t\t\t\t<p>Sensors tell the controller whether reality matches the program. Cameras locate parts, encoders report joint positions, force sensors detect contact, and safety sensors detect humans or obstacles. Without sensing, the robot can only repeat fixed motions and hope the scene has not changed.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-industrial-2\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">03<\/span>Convert evidence into coordinates<\/h4>\n\t\t\t\t\t\t\t<p>The robot must know where the part is relative to the camera, conveyor, fixture, tool and robot base. Calibration turns sensor evidence into a shared coordinate frame.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-industrial-3\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">04<\/span>Choose a safe trajectory<\/h4>\n\t\t\t\t\t\t\t<p>Motion planning computes how the robot should move from its current pose to the target pose. It must respect joint limits, payload, reachability, tool orientation, fixtures, cycle time and collision zones.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-industrial-4\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">05<\/span>Move with feedback<\/h4>\n\t\t\t\t\t\t\t<p>Motors, drives, valves and grippers execute the planned motion. Feedback loops compare intended and actual movement many times per second and correct position, speed, torque or force. This is where software becomes physical behavior.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-industrial-5\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">06<\/span>Check the result<\/h4>\n\t\t\t\t\t\t\t<p>A robot should not assume the task succeeded. Cameras, torque curves, force traces, weight checks or downstream quality stations confirm whether the part was picked, placed, tightened, measured or rejected correctly.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-industrial-6\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">07<\/span>Limit what may happen<\/h4>\n\t\t\t\t\t\t\t<p>Robotics creates physical risk. Safety zones, speed limits, collaborative-force settings, emergency stops, maintenance modes and human override define what the machine is allowed to do. Safety is not an add-on to robotics; it is part of the control system.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-industrial-7\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"industrial\"\n\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">08<\/span>Learn from operation<\/h4>\n\t\t\t\t\t\t\t<p>Cycle times, failed picks, quality results, sensor traces and maintenance data can improve the system through better tooling, updated rules, predictive maintenance or machine-learning models. Improvement should be validated by an engineer, logged and re-qualified before it affects motion.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-autonomous-0\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\tdata-step=\"0\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">01<\/span>Set the goal and boundaries<\/h4>\n\t\t\t\t\t\t\t<p>An autonomous robot needs more than a destination. It needs an operating design domain: where it may move, how close it may get to people, when it must slow down, what conditions are outside its design limits, and when a human must take over.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-autonomous-1\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\tdata-step=\"1\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">02<\/span>Sense the environment<\/h4>\n\t\t\t\t\t\t\t<p>Cameras, lidar, radar, ultrasonic sensors, GPS, inertial sensors, wheel encoders or tactile sensors provide partial evidence. Each sensor has failure modes: glare, rain, dust, occlusion, reflections, poor lighting, weak GPS or vibration.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-autonomous-2\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\tdata-step=\"2\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">03<\/span>Estimate position and uncertainty<\/h4>\n\t\t\t\t\t\t\t<p>The robot must estimate where it is, how fast it is moving and how certain that estimate is, matching live sensor data against a stored map or building one as it goes. When localization confidence weakens, safe behavior should become more conservative.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-autonomous-3\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\tdata-step=\"3\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">04<\/span>Build a working scene model<\/h4>\n\t\t\t\t\t\t\t<p>Sensor evidence becomes a structured world model: free space, obstacles, humans, vehicles, doors, curbs, stairs, terrain and moving objects. The system must also predict where moving agents are likely to go next.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-autonomous-4\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\tdata-step=\"4\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">05<\/span>Choose the next movement<\/h4>\n\t\t\t\t\t\t\t<p>Planning happens at several levels: route planning, local path planning, obstacle avoidance, speed selection and recovery behavior. The system should not only ask whether it can reach the goal, but whether it can reach it safely under current uncertainty.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-autonomous-5\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\tdata-step=\"5\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">06<\/span>Control the body<\/h4>\n\t\t\t\t\t\t\t<p>The plan becomes movement through motors, brakes, steering, wheels, legs or joints. A vehicle controls steering, acceleration and braking. A walking robot must also balance, place feet and handle uneven contact. Feedback keeps intended and actual motion aligned.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-autonomous-6\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\tdata-step=\"6\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">07<\/span>Handle surprises<\/h4>\n\t\t\t\t\t\t\t<p>Open-world robotics is tested by surprises: a person steps into the path, a sensor is blinded, the floor becomes slippery or another vehicle behaves unpredictably. Safe autonomy means slowing, stopping, rerouting, asking for help or handing control to a human.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t<section\n\t\t\t\t\t\t\tid=\"ns-aix-4-panel-autonomous-7\"\n\t\t\t\t\t\t\tclass=\"ns-aix-panel\"\n\t\t\t\t\t\t\tdata-flow=\"autonomous\"\n\t\t\t\t\t\t\tdata-step=\"7\"\n\t\t\t\t\t\t\taria-hidden=\"true\"\n\t\t\t\t\t\t>\n\t\t\t\t\t\t\t<h4><span class=\"ns-aix-panel-num\">08<\/span>Prove that autonomy is controlled<\/h4>\n\t\t\t\t\t\t\t<p>Autonomous robots need logs, safety cases, operating-domain limits, update control, cybersecurity, privacy rules, human override and liability handling. Because AI-driven behavior is harder to trace than conventional logic, governance must be built around the robot before it is trusted with physical action.<\/p>\n\t\t\t\t\t\t<\/section>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<div class=\"ns-aix-aspects\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset ns-on\" data-flow=\"industrial\" data-step=\"0\" aria-hidden=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Background<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The first industrial robots joined production lines in the 1960s for repetitive tasks such as welding and assembly; sensing and planning later turned fixed arms into adaptive systems.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Robotics combines mechanics, electronics and software: sensors to perceive, actuators to act and a control system to coordinate.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"industrial\" data-step=\"1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>A robot runs a continuous cycle of sensing, planning and acting, many times per second.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Cameras, encoders, force and safety sensors each answer a different question about the scene \u2014 together they replace guesswork with evidence.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"industrial\" data-step=\"2\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Calibration links camera, conveyor, tool and robot base into one shared coordinate frame \u2014 the bridge from perception to motion.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>A small calibration error can become a failed pick, a poor weld, a collision or a quality defect.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"industrial\" data-step=\"3\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Candidate paths are checked against joint limits, payload, reach, tool orientation, fixtures and collision zones before any real motion starts.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The useful path is not simply the shortest path; it is the path that can be executed safely and repeatably.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"industrial\" data-step=\"4\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Servo control runs thousands of times per second: each cycle measures the joint state and nudges the drives \u2014 which is why industrial motion looks smooth and lands repeatably.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>In simple industrial robots movements are pre-programmed and repeated precisely; advanced systems adapt to changing conditions using machine learning and real-time feedback.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"industrial\" data-step=\"5\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Verification turns blind automation into a controlled process \u2014 every cycle produces evidence instead of assumptions.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>A failed check can trigger an automatic retry, divert the part or stop the line \u2014 the response to bad evidence is designed in advance, not improvised.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"industrial\" data-step=\"6\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The safety envelope overrides production speed: zones, limits, emergency stops and human override are engineered into the controller itself.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Mechanical wear, battery limits and system failures create physical risk to people and equipment and demand ongoing maintenance.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"industrial\" data-step=\"7\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>What robotics can do<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Repetitive, dangerous and high-precision tasks; warehouse transport; vacuuming homes; exploring hazardous or far-away spaces.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Open-world autonomy remains hard: driving under all conditions like a human, or child-level dexterity in a humanoid, is still a major and expensive challenge.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"autonomous\" data-step=\"0\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The goal is constrained by the operating domain \u2014 autonomy without explicit boundaries is not a design, it is a hazard.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Navigating open space like a road or terrain remains one of the hardest problems in robotics, requiring significant sensing and computing power.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"autonomous\" data-step=\"1\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Sensors collect data about distance, position, temperature or visual content; the control system fuses this evidence into a picture of the surroundings.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Sensor evidence is useful but never perfect \u2014 robust systems assume degraded input and cross-check between sensors.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"autonomous\" data-step=\"2\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>GPS, odometry, inertial sensors, landmarks, lidar and visual cues are combined \u2014 and can disagree; the estimate carries an uncertainty, not a certainty.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Uncertainty must be carried into decisions: a robot that ignores how unsure it is will act with false confidence.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"autonomous\" data-step=\"3\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>The scene model is a changing estimate used to decide what motion remains safe \u2014 not a perfect understanding of the world.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>A warehouse robot uses this model to avoid obstacles while navigating toward a target location.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"autonomous\" data-step=\"4\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Motion planning algorithms let a robot navigate safely, often combined with computer vision for object recognition and machine learning for improvement over time.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Unsafe candidate paths must fade before motion starts \u2014 planning is where risk is filtered out.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"autonomous\" data-step=\"5\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>How it works<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Control runs much faster than planning: while a new route is computed a few times per second, balance and wheel or joint control correct the motion hundreds of times per second.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Creating humanoid robots that walk and use their hands dexterously is still far from the ability even a small child has.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"autonomous\" data-step=\"6\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Key concept<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Unexpected events should trigger conservative behavior \u2014 degradation to a safe state is a designed feature, not a failure.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>In practice<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>A delivery robot that stops and requests remote assistance is behaving correctly, not failing.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspectset\" data-flow=\"autonomous\" data-step=\"7\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Why it matters<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Controlled autonomy requires evidence: without logs and safety cases, trust in a physical AI system cannot be justified.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"ns-aix-aspect\">\n\t\t\t\t\t\t\t\t\t\t<h5>Watch out<\/h5>\n\t\t\t\t\t\t\t\t\t\t<p>Robots require substantial engineering, maintenance and safety effort; failures can cause physical damage to people and things.<\/p>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n\t<\/div><\/div><\/div><div class=\"et_pb_column_22 et_pb_column et_pb_column_1_3 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_15 et_pb_text et_pb_bg_layout_light et_pb_module et_flex_module preset--group--divi-text--divi-box-shadow--default preset--group--divi-text--divi-font-body--h1yjkjr--7p5s44libg preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><h2>The Challenges<\/h2>\n<p style=\"text-align: justify;\">Robotics, particularly when it comes to robots navigating an open space like a road or terrain, is still a very difficult field, requiring\u00a0 significant sensing and computing power. Making an autonomous car drive under all conditions like a human driver is still a significant and expensive challenge. And creating humanoid robots that can walk and dextrously use their \"hands\" is still far away from reaching the ability even a small child has when it comes to processing sensory input and turning it into smooth and seamless motion.<\/p>\n<p style=\"text-align: justify;\">Robots also require substantial engineering effort, maintenance, and safety considerations. Battery life, mechanical wear, and system failures can limit performance and create risk of physical damage to people and things.<\/p>\n<\/div><\/div><\/div><\/div><\/div><div class=\"et_pb_section_9 et_pb_section et_section_regular et_block_section preset--group--divi-section--divi-box-shadow--default preset--group--divi-section--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_row_14 et_pb_row et_block_row preset--group--divi-row--divi-box-shadow--default preset--group--divi-row--divi-sizing--h1k452m--default\"><div class=\"et_pb_column_23 et_pb_column et_pb_column_2_3 et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_text_16 et_pb_text et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-text--divi-box-shadow--default preset--module--divi-text--default\"><div class=\"et_pb_text_inner\"><p>Understanding these different AI approaches is essential when designing real-world AI systems. Each method has distinct strengths and limitations, which must be carefully considered when selecting the right approach for a given problem.<\/p>\n<p>If you want to see how we put these approaches to work, <a href=\"\/transformation\/\">move to what we can do for you<\/a>.<\/p>\n<\/div><\/div><\/div><div class=\"et_pb_column_24 et_pb_column et_pb_column_1_3 et-last-child et_block_column et_pb_css_mix_blend_mode_passthrough preset--group--divi-column--divi-box-shadow--default preset--group--divi-column--divi-sizing--hsj9uxo--default\"><div class=\"et_pb_module et_pb_button_module_wrapper et_pb_button_1_wrapper preset--group--divi-button--divi-box-shadow--default_wrapper preset--group--divi-button--divi-box-shadow--h1j7zeq--default_wrapper\"><a class=\"et_pb_button_1 et_pb_button et_pb_bg_layout_light et_pb_module et_block_module preset--group--divi-button--divi-box-shadow--default preset--group--divi-button--divi-box-shadow--h1j7zeq--default\" href=\"\/transformation\/\" style=\"text-wrap:balance\">9senses AI Transformation<\/a><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>9senses view on Artificial Intelligence is driven by a realistic view of the opportunities, limits and risks that AI comes with. It is grounded in a view that even though it very well simulates intelligence, it isn&#8217;t consciously intelligent and thus needs oversight.<\/p>","protected":false},"author":20,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-227133","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.9senses.ai\/de\/wp-json\/wp\/v2\/pages\/227133","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.9senses.ai\/de\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.9senses.ai\/de\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.9senses.ai\/de\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/www.9senses.ai\/de\/wp-json\/wp\/v2\/comments?post=227133"}],"version-history":[{"count":292,"href":"https:\/\/www.9senses.ai\/de\/wp-json\/wp\/v2\/pages\/227133\/revisions"}],"predecessor-version":[{"id":228227,"href":"https:\/\/www.9senses.ai\/de\/wp-json\/wp\/v2\/pages\/227133\/revisions\/228227"}],"wp:attachment":[{"href":"https:\/\/www.9senses.ai\/de\/wp-json\/wp\/v2\/media?parent=227133"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}